An energy internet data security transmission and authentication system

Abstract

The present application relates to the technical field of data security protection, and more particularly to an energy internet data security transmission and authentication system, the present application proposes the following scheme, through adaptive block capacity adjustment, the block size is dynamically adjusted according to energy data flow, improve storage efficiency.Adopt the credibility rating mechanism, data is allocated to the main block chain or the auxiliary block chain, optimize storage resources and improve data credibility.In the cross-chain interaction process, combined with identity authentication, zero-knowledge proof, time lock and other technologies, improve the security and tamper resistance of data transmission.Optimize block storage based on energy supply and demand balance state, reduce write load, ensure efficient storage of data in different load environments.The present application improves the storage flexibility, security and credibility of energy data, and can be widely applied in smart grid, distributed energy trading, carbon emission monitoring and other fields.

Description

Technical Field

[0001] This invention relates to the field of data security protection technology, and in particular to an energy internet data security transmission and authentication system. Background Technology

[0002] In recent years, the rapid development of energy internet technology has generated massive amounts of energy data in scenarios such as smart grids, distributed energy management, and energy trading markets. This data involves multiple stages including power generation, transmission, distribution, consumption, and transaction settlement, making its secure storage, transmission, and authentication critical issues that urgently need to be addressed. Traditional energy data management methods mainly rely on centralized databases or traditional cloud storage. While these methods can provide some data storage and computing capabilities, they suffer from numerous technical bottlenecks in areas such as data credibility management, tamper-proof security, and cross-system interoperability, making it difficult to meet the demands of the modern energy internet for data security, efficiency, and decentralized storage.

[0003] Currently, blockchain technology, due to its decentralized, immutable, and highly traceable characteristics, is widely used in energy data management and energy trading. Traditional blockchain storage methods use fixed-size blocks for data storage, which has limitations in the energy internet environment. Firstly, energy data flow fluctuates significantly. For example, during peak renewable energy periods (such as photovoltaic power generation), data transaction volume surges, while during low-load periods, the data volume decreases significantly. Fixed-capacity blocks can lead to wasted storage resources or data congestion. Secondly, the reliability of energy data varies considerably. Data from smart meters is generally reliable, while data submitted by distributed energy users may contain biases or errors. Traditional blockchain storage methods cannot distinguish the reliability of data, leading to unreliable data occupying storage resources and even affecting the fairness of transactions. Furthermore, the need for multi-party data sharing in the energy internet exacerbates the problem of cross-chain data interaction between blockchains. Existing technologies lack effective identity verification, privacy protection, and data integrity verification mechanisms, which can easily lead to cross-chain data tampering, unclear transaction identities, and reduced data consistency, thereby affecting the normal operation of the energy trading market.

[0004] For example, Chinese patent CN108564471B discloses a smart energy internet security trading system and method based on blockchain technology. The system includes a blockchain energy trading platform, terminal equipment, an energy dispatching system, and a smart contract system. The terminal equipment interacts with the blockchain energy trading platform to exchange energy supply and demand information; the blockchain energy trading platform interacts with the energy dispatching system to exchange energy supply and demand information and energy trading plans; and the blockchain energy trading platform interacts with the smart contract system to exchange smart contract information. This invention combines blockchain technology and smart contracts, eliminating the role of a third-party trading center in the energy trading process. All participants in the transaction are on equal footing, and energy transactions are automatically executed according to pre-set smart contracts.

[0005] The above-mentioned existing technologies all suffer from the problems mentioned in the background. In order to solve the above problems, this application designs an energy internet data security transmission and authentication system. Summary of the Invention

[0006] The technical problem this invention addresses is the shortcomings of existing technologies. It provides an energy internet data security transmission and authentication system that improves storage efficiency by dynamically adjusting block size based on energy data traffic through adaptive block capacity adjustment. A trustworthiness rating mechanism is employed to allocate data to the main or secondary blockchain, optimizing storage resources and enhancing data trustworthiness. During cross-chain interactions, technologies such as identity verification, zero-knowledge proofs, and time locks are combined to improve data transmission security and tamper resistance. Furthermore, block storage is optimized based on energy supply and demand balance, reducing write load and ensuring efficient data storage under varying load conditions. This method enhances the flexibility, security, and trustworthiness of energy data storage and can be widely applied in smart grids, distributed energy trading, carbon emission monitoring, and other fields.

[0007] To achieve the above objectives, the present invention provides the following technical solution:

[0008] An energy internet data security transmission and authentication system is applied to energy internet data. The data security transmission and authentication system includes a data acquisition module, a data storage module, and a data protection module.

[0009] The data acquisition module is used to collect energy data from the energy internet;

[0010] The data storage module is configured with a block configuration strategy, which is used to adjust the block size and storage method according to the characteristics and flow changes of energy data.

[0011] The data protection module is used to provide data encryption and security authentication mechanisms.

[0012] The data storage module includes:

[0013] A storage allocation unit is used to perform a credibility rating on the energy data and allocate storage areas according to the credibility.

[0014] A capacity adjustment unit is used to adjust the capacity of the storage area based on the flow fluctuations of the energy data.

[0015] The block configuration strategy includes a trustworthiness assessment logic and a block adjustment logic. The trustworthiness assessment logic is configured within the storage allocation unit, and the block adjustment logic is configured within the capacity adjustment unit.

[0016] The credibility assessment logic includes:

[0017] Calculate the credibility weight of each energy transaction in the energy data;

[0018] The credibility weight is compared with a preset credibility threshold. If it is greater than or equal to the credibility threshold, the energy transaction data is allocated to the main blockchain.

[0019] If the value is less than the trust threshold, the energy transaction data will be allocated to the secondary blockchain.

[0020] The block adjustment logic includes:

[0021] Predicting data flow using time-series forecasting models;

[0022] When an increase in energy data traffic is predicted, the storage capacity is expanded according to the predicted amount.

[0023] When a decrease in energy data traffic is predicted, the capacity of the storage area is reduced based on the predicted amount.

[0024] The data storage module further includes:

[0025] A data interaction unit, which provides data interaction logic between the main blockchain and the secondary blockchain;

[0026] The supply and demand adjustment unit is used to monitor the supply and demand balance of the energy internet. When the supply and demand balance is imbalanced, the block capacity in the main blockchain is adjusted.

[0027] The data interaction logic includes:

[0028] The data on the secondary blockchain is authenticated, and the data on the main blockchain is retrieved after successful authentication.

[0029] Encrypting data in the main blockchain and secondary blockchain using zero-knowledge proofs;

[0030] Based on the interaction results, the credibility weight of the data involved in the transaction is updated to determine whether the data transfer conditions are met.

[0031] If the data transfer conditions are met, a second verification is performed using a time lock. Once the verification is successful, the data transfer is initiated.

[0032] The authentication of data on the secondary blockchain includes:

[0033] A physical layer signature is generated based on the power line carrier communication characteristics corresponding to the data in the secondary blockchain.

[0034] The main blockchain receives and verifies the physical layer signature.

[0035] The adjustment of block size in the main blockchain includes:

[0036] The trust threshold is adjusted according to the emergency consensus mechanism to reduce the data writing frequency of the main blockchain;

[0037] Based on the direction of imbalance, energy data in the main blockchain is prioritized and categorized. Selected energy data is then stored in the secondary blockchain, with the energy data being filtered according to its priority.

[0038] The prioritization of energy data in the main blockchain based on the direction of imbalance includes:

[0039] Adjust the consensus algorithm parameters of the main blockchain according to the direction and severity of the supply and demand imbalance;

[0040] The priority of energy data in the main blockchain is adjusted according to the consensus algorithm parameters.

[0041] The adjustment of the consensus algorithm parameters of the main blockchain includes:

[0042] When energy supply cannot meet demand, increase the staking weight of proof-of-stake nodes and increase the decision-making weight of reputation nodes in the consensus process.

[0043] When market volatility exceeds a set volatility threshold, shorten the voting cycle of Byzantine fault tolerance.

[0044] A method for secure data transmission and authentication in the energy internet, the method comprising:

[0045] Collect energy data from the energy internet;

[0046] The block capacity in the energy blockchain is adjusted according to the energy data, wherein each block corresponds one-to-one with the characteristics of the energy data;

[0047] Energy data is transmitted to the corresponding blocks and stored in shards according to the type of energy data.

[0048] The method also includes encrypting the energy data based on its physical characteristics and storage location.

[0049] Compared with the prior art, the beneficial effects of the present invention are:

[0050] This invention can dynamically adjust the block size according to fluctuations in energy data flow, avoiding waste or congestion of storage resources; it adopts a trust rating mechanism to ensure that high-trust data is stored first on the main blockchain and low-trust data is stored on the secondary blockchain, improving data reliability and storage efficiency; through a supply and demand balance optimization mechanism, it reduces the writing of low-priority data to the main blockchain during peak energy trading periods, reducing storage pressure; and by combining technologies such as identity verification, zero-knowledge proof, and time lock, it ensures the security of cross-chain data interaction and prevents data tampering and illegal storage. Attached Figure Description

[0051] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0052] Figure 1 This is a flowchart illustrating a method for secure data transmission and authentication in the energy internet according to Embodiment 1 of the present invention.

[0053] Figure 2 This is a module diagram of an energy internet data security transmission and authentication system according to Embodiment 2 of the present invention. Detailed Implementation

[0054] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0055] Example 1:

[0056] Please see Figure 1 This invention provides an embodiment of a method for secure data transmission and authentication in the energy internet. This method addresses key issues such as large fluctuations in energy internet data traffic, low utilization of data storage resources, and insufficient data security through intelligent data management, dynamic block capacity adjustment, trusted storage layering, and security authentication technologies. This makes the storage, transmission, and authentication of energy data more efficient, secure, and reliable. The specific steps are as follows:

[0057] S1: Collect energy data from the energy internet;

[0058] In this embodiment, energy data is first collected. In the context of the energy internet, data acquisition devices such as smart meters, substation monitoring equipment, distributed generation facilities, charging pile terminals, and new energy management systems are used to acquire energy data in real time. The collected energy data includes, but is not limited to, power generation data, power distribution data, power consumption data, transaction settlement data, and energy consumption analysis data. These data come from diverse sources, have varying degrees of reliability, and are complex in data type. For example, transaction settlement data has high reliability, while data collected by distributed energy devices may be affected by noise and has lower reliability. Therefore, a standardized data preprocessing procedure is adopted, including data formatting, outlier detection, and data cleaning, to ensure data quality and prepare for subsequent reliability rating and blockchain storage.

[0059] S2: Adjust the block size in the energy blockchain based on energy data, where each block corresponds one-to-one with the characteristics of the energy data;

[0060] In this embodiment, due to the significant fluctuations in energy data flow, a fixed block capacity could lead to excessive data storage pressure during peak periods or resource waste during off-peak periods. Therefore, an adaptive block partitioning strategy is adopted to monitor energy data flow in real time and, combined with a time-series prediction algorithm, predict future energy data trends and dynamically adjust the block capacity accordingly. For example, when an increase in the frequency of new energy grid-connected transactions is detected, the system automatically expands the block capacity to improve data throughput and ensure that high-priority data can be written in a timely manner. When a low-load period is detected (such as low-electricity trading periods at night), the block capacity is automatically reduced to avoid invalid storage space occupation and improve blockchain storage efficiency. This method can solve the problems of uneven energy data storage load, waste of storage resources, or overflow caused by the rigid fixed storage capacity of traditional blockchains, improve storage flexibility, and ensure the adaptability of blockchain in different energy trading scenarios.

[0061] S3: Transmit energy data to the corresponding block and store it in fragments according to the type of energy data;

[0062] In this embodiment, energy data is stored using a tiered storage strategy combined with a trust rating mechanism. For the collected energy data, its trust score is first calculated, which is quantitatively evaluated based on multiple factors such as data source, data integrity, historical transaction records, and smart contract execution. Subsequently, based on the trust rating results, a primary and secondary blockchain storage model is adopted, i.e.:

[0063] When the credibility weight is greater than or equal to the preset credibility threshold, the energy data will be directly stored in the main blockchain. The main blockchain uses a network-wide consensus mechanism for storage to ensure the high credibility and immutability of the data.

[0064] When the credibility weight is less than the preset credibility threshold, the energy data will be stored in the secondary blockchain. The secondary blockchain provides flexible storage methods and allows low-credibility data to be transferred to the main blockchain after its credibility is improved through secondary verification or data interaction.

[0065] This application not only optimizes the storage resources of the main blockchain and avoids low-trust data from occupying too much storage space, but also provides a secure and scalable data storage mechanism, allowing low-trust data to still have the opportunity to enter the main blockchain through the trust enhancement mechanism, thereby improving the system's compatibility and data integrity.

[0066] S4: Encrypt the energy data based on its physical characteristics and storage location;

[0067] In this embodiment, to ensure data security and tamper resistance, a multi-layered encryption mechanism is used to encrypt energy data, specifically including:

[0068] Physical layer encryption: A physical layer signature is generated by combining the physical characteristic parameters of energy data (such as power line carrier signal characteristics, voltage frequency changes, and current waveform characteristics), and this signature is attached to the data storage record to prevent the data from being tampered with or forged during storage or transmission.

[0069] Zero-knowledge proof (ZKP) encryption: For blockchain data interaction processes, zero-knowledge proof technology is used to verify the legality and integrity of transactions without revealing the specific content of energy transactions, ensuring the security of cross-chain data interaction and preventing man-in-the-middle attacks or illegal data tampering.

[0070] Time-locking mechanism: When data interaction occurs between the main blockchain and the secondary blockchain, the system dynamically adjusts the time-locking strategy based on the trust rating. For example, for data with low trust, a time-locking delay storage mechanism is required before the data is migrated to the main blockchain, along with additional smart contract verification, to ensure data security.

[0071] While blockchain technology has been widely applied to energy data management, it still faces several technical bottlenecks, including fixed block storage capacity, insufficient differentiation of data credibility, uneven storage load due to fluctuations in energy supply and demand, and inadequate security for cross-chain data interaction. Traditional blockchain storage methods typically employ fixed block sizes. When energy transaction data volumes surge, this can easily lead to storage congestion and increased computational load. Conversely, during periods of low data volume, fixed-capacity blocks may result in wasted storage resources. Furthermore, existing blockchain systems often store all energy data indiscriminately on the main blockchain, failing to differentiate data credibility and leading to inefficient use of storage resources. This can even compromise the overall reliability of the data due to the storage of untrusted data. Simultaneously, in the energy internet, energy supply and demand balance fluctuates. When the power grid experiences large-scale renewable energy integration, peak electricity consumption, or sudden load changes, traditional blockchains cannot dynamically adjust their storage strategies, resulting in transaction confirmation delays and even block congestion. Moreover, in cross-chain data interaction, existing technologies lack secure verification mechanisms, making them prone to data tampering, unidentified cross-chain transactions, and inconsistent transaction information. This reduces the credibility of energy data, thereby affecting the fairness of the electricity trading market and the accuracy of energy dispatch.

[0072] Specifically, this embodiment employs an adaptive block capacity adjustment strategy based on data flow, enabling the blockchain storage capacity to dynamically expand or shrink according to changes in energy data flow. For example, during periods of high activity in new energy trading, such as peak photovoltaic power generation or periods of significant fluctuation in the electricity trading market, the system automatically expands the block capacity to increase data throughput, ensuring that transaction data can be written to the blockchain in a timely manner and avoiding data loss or delays due to insufficient capacity. Conversely, during periods of low electricity demand or when data flow is low, the block capacity will automatically shrink to reduce storage resource waste and improve overall computing efficiency. Compared to the fixed-capacity storage method of traditional blockchains, this method significantly improves the storage flexibility of energy data and the scalability of the blockchain, ensuring efficient operation under different load environments.

[0073] Furthermore, this embodiment utilizes a credibility rating mechanism to categorize and store energy data, thereby optimizing the allocation of storage resources. Specifically, the system calculates credibility weights for energy data generated from sources such as smart meters, photovoltaic inverters, wind power monitoring systems, and distributed energy management platforms. This calculation is based on multiple factors, including the reliability of the data source, data integrity, transaction records, and smart contract execution. Energy transaction data with a credibility weight greater than a preset threshold is stored on the main blockchain, which employs a network-wide consensus mechanism to ensure the immutability and authenticity of the data. Energy data with lower credibility is stored on a secondary blockchain, which allows for further verification before being transferred to the main blockchain, or a lighter storage method to reduce storage costs. For example, in the process of power grid load forecasting, historical electricity consumption data and real-time collected data have different credibility levels. Traditional methods struggle to distinguish the storage priorities of these data, while this method intelligently allocates storage locations, ensuring that high-credibility data is stored first, while low-credibility data does not occupy main blockchain storage resources, thus improving the overall storage efficiency and data credibility of the blockchain.

[0074] Furthermore, this embodiment introduces a block optimization strategy based on the energy supply and demand balance. When energy supply and demand are imbalanced (such as a surge in electricity demand or a supply shortage), the system dynamically adjusts the trust threshold, reduces the data write frequency to the main blockchain, and adjusts the storage strategy according to data priority. For example, when large-scale renewable energy generation is connected to the grid, the system lowers the trust threshold, allowing more renewable energy transaction data to enter the main blockchain to ensure the integrity of market transactions. During normal electricity load periods, the system raises the trust threshold, storing only the most critical transaction data to reduce storage load. This approach effectively mitigates the impact of supply and demand imbalances on blockchain storage, improves the system's robustness, and enables it to adapt to dynamic changes in the energy market. Compared to the static storage method of traditional blockchains, this method can better cope with energy supply and demand fluctuations and ensure the timeliness and reliability of energy data.

[0075] Furthermore, regarding cross-chain data interaction, this embodiment combines security authentication mechanisms such as physical layer signatures, zero-knowledge proofs, and time locks to enhance the security of data interaction. Specifically, a unique physical layer signature is generated using the physical layer characteristics of power line carrier signals and is matched and verified during data interaction to ensure the authenticity of the transaction data source. Simultaneously, a zero-knowledge proof (ZKP) mechanism is employed to complete data consistency verification without disclosing energy transaction details, preventing data tampering or identity forgery during cross-chain interactions. In addition, for data interaction between the main and secondary blockchains, this method introduces a time lock mechanism to ensure that low-credibility data can only be migrated to the main blockchain after a sufficiently long period of security verification, preventing data fraud or illegal tampering. For example, in a new energy trading market, a transaction record might initially be stored on the secondary blockchain due to low data source credibility. However, after a period of time, through confirmation and consensus verification by multiple trading parties, the credibility of the data increases, triggering the time lock unlocking mechanism to migrate the data to the main blockchain, thus improving the data's credibility. This approach not only improves the security of cross-chain data but also effectively reduces the risk of mis-storage and illegal tampering.

[0076] For example, suppose in an electricity trading market, a large number of distributed photovoltaic power generation devices sell surplus electricity to the grid. Traditional blockchains, due to their fixed block capacity limitations, may experience data storage congestion during peak trading periods, leading to transaction confirmation delays and even storage resources being occupied by low-value data. This embodiment addresses this by adaptively adjusting block capacity to automatically expand block capacity during peak trading periods, increasing data throughput. Simultaneously, a trust rating mechanism ensures that high-trust transaction data is prioritized for storage on the main blockchain, while low-trust data is stored only on secondary blockchains, avoiding wasted storage resources. Furthermore, through energy supply and demand balance monitoring, the trust threshold and storage strategy are dynamically adjusted when there is a severe imbalance in electricity supply and demand, ensuring stable system operation. Finally, physical layer signatures and zero-knowledge proofs guarantee the security and integrity of cross-chain transaction data. This combination of technologies enables this application to achieve accurate storage, trusted authentication, and efficient transmission of energy data in high-load, complex energy market environments, offering higher storage efficiency, stronger security, and better adaptability to energy markets compared to traditional technologies.

[0077] The specific steps of S2 are as follows:

[0078] S2.1: Adjust the capacity of the storage area based on the flow fluctuations of the energy data;

[0079] Specifically, in the energy internet, due to the volatility of factors such as energy trading, electricity load, and renewable energy generation, the flow of energy data is not uniform and stable, but rather exhibits distinct temporal and spatial characteristics. For example, during peak electricity consumption periods or when new energy sources are being integrated on a large scale, the volume of energy data transactions surges, while at night or during periods of low load, the data flow is relatively small. If a fixed-capacity storage area is used, it may lead to insufficient storage resources and blockchain network congestion during high traffic periods, while resulting in wasted storage space during low traffic periods.

[0080] In this embodiment, when an increase in energy data traffic is predicted, the system will automatically expand the capacity of the storage area to provide greater data storage capacity, while increasing the size and number of write blocks to avoid data backlog or transaction delays; when a decrease in data traffic is predicted, the system will appropriately reduce the capacity of the storage area to reduce unnecessary storage resource occupation, improve storage efficiency, and reduce computing resource consumption.

[0081] Furthermore, under high traffic conditions, the system dynamically adjusts the block packaging frequency, increasing the new block generation rate in a short period to reduce transaction backlog and improve the write speed of energy data. Under low traffic conditions, the frequency of new block generation is reduced to decrease computational costs and storage overhead. This effectively adapts to changes in energy data traffic, ensuring that blockchain storage resources remain optimally configured under different data traffic conditions, thereby improving storage resource utilization, reducing storage costs, and avoiding storage crashes or computational bottlenecks caused by sudden traffic surges.

[0082] S2.2: Monitor the supply and demand balance of the energy internet;

[0083] Specifically, in the energy internet environment, supply and demand balance is a key factor in determining the stable operation of the power grid. Especially when a high proportion of renewable energy is connected, supply and demand fluctuations may lead to drastic changes in electricity market prices and frequent fluctuations in energy transactions, which in turn affect the stability of energy data storage and transmission.

[0084] In this embodiment, by accessing data sources such as the power dispatch center, smart meters, and load forecasting systems, the system monitors the changing trends of energy supply and demand in real time, calculates the current supply and demand status of the power grid, including indicators such as grid load level, generation output power, user electricity demand, renewable energy output forecast, and market trading activity, and establishes a supply and demand balance analysis model by combining information such as blockchain storage load, transaction data volume, and historical supply and demand status. When supply and demand fluctuations are detected, the system analyzes their potential impact on data flow and storage demand. For example, when renewable energy output is high, new energy transaction data may increase, and the system will predict the blockchain storage pressure in advance and prepare adaptive storage expansion solutions; when electricity demand decreases and trading market activity decreases, the system will reduce the generation of new blocks and optimize storage strategies to reduce the storage burden of low-priority data. At the same time, the system will feed back the monitored supply and demand status to the blockchain smart contract to achieve deep collaborative optimization between blockchain storage and power grid operation. For example, when new energy fluctuations are large, the smart contract can automatically adjust the blockchain data storage strategy, prioritizing the storage of high-reliability new energy transaction data and delaying the writing of low-priority market data to ensure the security and stability of electricity transaction information. In this way, this application can keep abreast of the changing trends of energy supply and demand in real time, and combined with the storage requirements of blockchain, ensure that data flow and storage resources in the energy Internet environment are in a dynamic balance, thereby improving the reliability and stability of data storage.

[0085] S2.3: When the supply and demand balance is disrupted, adjust the block capacity in the main blockchain;

[0086] Specifically, when supply and demand imbalances occur, they can lead to sharp fluctuations in electricity market transactions, thereby affecting the demand for energy data storage. For example, when the grid load suddenly surges and power supply capacity is insufficient, a large number of dispatch transactions and market adjustment transactions will surge. If the blockchain storage capacity is fixed, it may lead to a backlog of transaction data storage, blockchain network congestion, affecting transaction confirmation time, and even causing data storage failure.

[0087] In this embodiment, the severity of the imbalance is first analyzed, including the magnitude and duration of the supply-demand discrepancy, transaction activity, and other factors, and the block capacity of the main blockchain is adjusted accordingly. For example, when electricity demand rises sharply and market transactions increase, the system automatically increases the block capacity of the main blockchain, increases data packaging and writing rates to quickly respond to market transaction demands, and ensures the integrity of power dispatch and transaction records; while after supply and demand gradually return to balance, the system gradually reduces the block capacity to reduce unnecessary data storage and computing resource consumption.

[0088] Furthermore, this application incorporates a data credibility rating mechanism to prioritize data during periods of supply-demand imbalance. For example, during power shortages, the system prioritizes storing high-value transaction data and real-time dispatch instructions, while low-value market forecast data, historical electricity consumption records, and other low-priority data can be temporarily stored in a secondary blockchain or a distributed storage solution can be adopted to reduce the storage burden on the main blockchain. Further, this technology incorporates a time-lock mechanism. For transaction data stored during periods of supply-demand imbalance, a delayed confirmation mechanism can be used to perform secondary verification of the stored data after supply and demand balance is restored. This ensures the accuracy of data written under abnormal conditions and prevents data errors or tampering risks caused by market fluctuations. Through this strategy, blockchain storage resources can be optimized during periods of supply-demand imbalance, ensuring efficient storage and dispatch of energy data, while avoiding transaction data backlog, improving transaction confirmation speed, and enhancing the stability and reliability of the blockchain system.

[0089] The specific steps of S2.1 are as follows:

[0090] S2.1.1: Predict data flow using a time-series forecasting model;

[0091] In this embodiment, to improve the blockchain's ability to rationally allocate energy data storage resources, a time-series prediction model is used to predict energy data flow. This allows the system to adjust storage area capacity in advance, avoiding storage congestion due to sudden increases in flow or waste of storage resources due to decreases in flow. This embodiment uses a Long Short-Term Memory (LSTM) network, an ARIMA (Autoregressive Integral Moving Average) model, and an Exponentially Weighted Moving Average (EWMA) model for data flow prediction. LSTM can effectively capture the long-term dependencies of energy data flow and is suitable for processing periodically fluctuating electricity trading data, while the ARIMA model is suitable for short-term load forecasting, and EWMA is used to smooth energy data flow in real time to reduce the impact of sudden changes. During the data flow prediction process, the system first collects historical transaction records and real-time electricity data from multiple data sources such as energy trading platforms, smart meters, and distribution network dispatch centers. Key parameters affecting flow fluctuations, such as transaction time, equipment load, market electricity price, weather conditions, and user electricity consumption habits, are extracted using feature extraction methods. Then, the system constructs time-series input data using the sliding window method and feeds it into the LSTM model for training. After training, the model can automatically capture the periodic changes in energy data flow, load change trends, and the impact of sudden trading events, ultimately deriving the predicted data flow values ​​for future times. To ensure the reliability of the prediction results, the system further combines ARIMA and EWMA for multi-model fusion based on LSTM prediction to improve the robustness and adaptability of the prediction, enabling the predicted values ​​to reflect both long-term trends and rapid responses to short-term changes.

[0092] S2.1.2: When an increase in energy data traffic is predicted, expand the capacity of the storage area according to the predicted amount;

[0093] In this embodiment, when the system predicts an increase in future energy data traffic using a time-series prediction model, it automatically executes a storage area expansion strategy to ensure sufficient storage resources are available during periods of high traffic, preventing transaction delays or storage failures due to insufficient blockchain capacity. Expanding the storage area capacity primarily involves techniques such as dynamically adjusting block size, increasing block generation frequency, and introducing a temporary cache storage mechanism. First, the system calculates the current utilization rate of the storage area and dynamically adjusts the block capacity based on the predicted traffic growth rate. For example, if energy trading volume is expected to increase by more than 50% over the next 10 block cycles, the system automatically increases the block capacity, allowing each block to store more energy trading data. Furthermore, the system dynamically adjusts the block generation frequency to accelerate the generation of new blocks, reducing transaction delays caused by single-block storage overflow. Under high traffic conditions, the system also temporarily activates a high-speed cache storage mechanism to store some low-priority data (such as historical transaction logs and device operation data) off-chain. Once the system load returns to normal, the cached data is written to the blockchain in batches to balance the storage pressure on the main blockchain. Through the aforementioned technical means, the system can ensure that data storage remains stable even during peak periods of large-scale energy trading, avoiding transaction delays and block overflow issues, and improving the blockchain's throughput and storage resilience.

[0094] S2.1.3: When a decrease in energy data traffic is predicted, reduce the capacity of the storage area according to the predicted amount;

[0095] In this embodiment, when the system predicts that energy data traffic will decrease in the future, it will implement a storage area reduction strategy to avoid wasting storage resources and optimize storage efficiency. Technical means of storage area reduction include reducing block size, lowering block generation frequency, compressing low-priority data, and merging storage areas. When traffic decreases, the system will first dynamically reduce the size of new blocks, enabling the blockchain to record energy data at a lower storage cost. For example, if it predicts that transaction volume will decrease by 70% in the next 30 block cycles, the system will automatically reduce the block size to 50% of its original size to reduce unnecessary storage space occupation. Simultaneously, the blockchain's block generation frequency will also decrease to reduce computing resource consumption and improve the overall energy efficiency of the system. Furthermore, the system will compress and merge low-priority data (such as expired transaction records and duplicated log data), merging multiple small blocks into a larger block to reduce blockchain storage fragmentation. For historical data that has not been accessed for a long time, the system can also enable a tiered storage mechanism to migrate it to a secondary blockchain or off-chain storage to further free up storage space on the main blockchain. Through the above strategies, the system can rationally allocate storage resources under low load conditions, improve storage utilization, and ensure the system's adaptability and storage efficiency under different load scenarios.

[0096] The specific steps of S2.3 are as follows:

[0097] S2.3.1: Adjust the trust threshold according to the emergency consensus mechanism to reduce the data writing frequency of the main blockchain;

[0098] Specifically, when the energy internet system detects an imbalance between energy supply and demand—such as large-scale grid connection of new energy sources, a sudden surge in grid load, or the triggering of demand-side response events—the system uses its built-in supply and demand status monitoring module to monitor energy data fluctuations in real time and compare them with historical load data to identify imbalance trends. To prevent write congestion, storage resource overload, and transaction confirmation delays caused by data surges during this period, the system triggers an emergency consensus mechanism to automatically adjust the trust threshold. This means increasing the main blockchain's data trust requirements to reduce the frequency of data writes to the main blockchain. Specifically, the system dynamically adjusts the minimum trust weight threshold of the main blockchain, such as increasing it from 70 to 85, so that only high-trust data can be stored in the main blockchain, while medium- and low-trust data is temporarily stored in the secondary blockchain, awaiting further verification or delayed storage. To ensure the rationality of adjusting the trust threshold, the system combines real-time energy market transaction data, historical load fluctuation models, and short-term forecasting algorithms (such as short-term load forecasting based on LSTM or random forests) to predict the trend of energy data over a future period, thereby determining the adjustment range and applicable period of the threshold. For example, when there is a sudden surge in renewable energy generation and the power grid becomes overloaded, the system not only raises the trust threshold but also simultaneously reduces the block write frequency, extending the data packaging time from 5 seconds to 15 seconds to reduce storage pressure. It prioritizes storing critical energy data, such as transaction settlement data and scheduling instructions, while low-value data (such as equipment operation logs and sensor redundancy data) is temporarily deferred. Furthermore, the emergency consensus mechanism can be combined with a voting mechanism, allowing main blockchain nodes to quickly vote on threshold adjustments to reach consensus. This ensures the strategy takes effect rapidly in emergencies, safeguards the stability of the main blockchain, and prevents slow system response or even system crashes due to block write overload.

[0099] S2.3.2: Prioritize the energy data in the main blockchain according to the direction of imbalance, and store the selected energy data in the secondary blockchain, where the energy data is selected according to the priority.

[0100] Specifically, when the system detects an imbalance between energy supply and demand, it first needs to determine the specific direction of the imbalance: whether it's an oversupply of electricity (energy surplus) or an oversupply of electricity (energy shortage) to implement appropriate data storage strategies. In the case of an oversupply, due to the large volume of electricity transactions in the market, the system prioritizes storing high-frequency transaction data and real-time electricity market settlement data. For some lower-priority data, such as electricity consumption logs during low-load periods, general energy consumption monitoring data, and some predictive data, a hierarchical data storage mechanism will automatically filter and store them in the secondary blockchain. This filtering mechanism uses an energy data classification algorithm, combined with historical transaction importance assessment, data access frequency analysis, and data integrity requirements, to ensure that data stored in the secondary blockchain does not affect the core decision-making data of the main blockchain. For example, renewable energy distributed transaction data with low transaction volume can be temporarily stored in the secondary blockchain, while high-value transaction data related to market pricing is stored in the main blockchain to ensure the fairness and timeliness of market transactions. On the other hand, when electricity demand exceeds supply, to ensure the smooth operation of the energy trading market, the system prioritizes storing electricity transactions, dispatch control instructions, and energy management system (EMS) data from large users, while transferring transaction data and predictive data from small distributed users to the secondary blockchain to reduce storage pressure on the main blockchain. Furthermore, to ensure the integrity and traceability of data on the secondary blockchain, the system employs data hash indexing technology. When data is stored on the secondary blockchain, its hash fingerprint is simultaneously recorded on the main blockchain, ensuring rapid verification of data integrity and consistency when data needs to be traced back. To further improve the data interaction efficiency between the main and secondary blockchains, the system uses an automatic smart contract triggering mechanism. When data on the secondary blockchain reaches a certain level of credibility or the market needs to retrieve data, the data in the secondary blockchain is automatically resubmitted to the main blockchain, and its authenticity is verified through zero-knowledge proofs. This dynamic data storage optimization scheme based on energy supply and demand balance allows the main blockchain to maintain efficient storage and rapid access to critical data, while the secondary blockchain undertakes the temporary storage and backup tasks for low-priority data, improving overall storage flexibility and ensuring the reliability of energy data and the stability of the system.

[0101] The prioritization of energy data in the main blockchain based on the direction of imbalance includes:

[0102] Adjust the consensus algorithm parameters of the main blockchain according to the direction and severity of the supply and demand imbalance;

[0103] The priority of energy data in the main blockchain is adjusted according to the consensus algorithm parameters.

[0104] The adjustment of the consensus algorithm parameters of the main blockchain includes:

[0105] When energy supply is insufficient, increase the staking weight of proof-of-stake nodes and enhance the decision-making power of reputation nodes in the consensus process;

[0106] When market trading is highly volatile, optimize the voting cycle of Byzantine fault tolerance to accelerate transaction confirmation.

[0107] In this embodiment, to ensure that the main blockchain can adapt to the volatility of the energy market under conditions of energy supply and demand imbalance, and to achieve reasonable priority classification and storage management of energy data, the system dynamically adjusts the consensus algorithm parameters. This allows the blockchain to adapt to different supply and demand states, improving transaction confirmation efficiency and data storage reliability. In the energy market, changes in supply and demand directly affect the blockchain data writing speed, consensus efficiency, and data storage strategy. Therefore, this method adjusts the core parameters of the consensus algorithm to achieve intelligent allocation of data priorities, ensuring that the system can still maintain efficient and stable operation even under conditions of insufficient energy supply or drastic transaction fluctuations.

[0108] When energy supply is insufficient, the staking weight of Proof-of-Stake (PoS) nodes is increased, enhancing the decision-making power of high-reputation nodes in the consensus process. Since insufficient energy supply is usually accompanied by a decrease in transaction volume but an increase in transaction importance, using the standard PoS weight calculation method in this situation could lead to low-reputation nodes influencing the consensus result, thereby increasing the risk of malicious attacks and data tampering. Therefore, in the event of insufficient supply, the system dynamically adjusts the parameters of the PoS consensus algorithm, increasing the weight of nodes with larger staking amounts and good historical transaction records in the voting process, giving high-reputation nodes greater voting power. Specifically, the system comprehensively evaluates nodes based on indicators such as historical consensus success rate, data storage integrity, number of transactions processed, and amount of staked assets, assigning higher consensus weights to the top 10%-20% of nodes in terms of reputation to ensure data authenticity and immutability. Furthermore, the system embeds automatic adjustment logic into the smart contract; when the system detects a state of insufficient energy supply (such as a drop in power load exceeding a certain threshold or power generation falling below safe operating levels), the PoS consensus mechanism automatically increases the voting weight of high-reputation nodes, reducing the influence of low-reputation nodes. This method can effectively improve the reliability of energy trading data, prevent malicious nodes from manipulating energy trading results through low-cost attacks during periods of energy shortage, and ensure that the data stored in the main blockchain is more accurate and reliable, providing accurate decision-making basis for subsequent market regulation.

[0109] When market transactions fluctuate dramatically, the voting cycle of Byzantine Fault Tolerance (PBFT) is optimized to accelerate transaction confirmation. Energy market transaction volumes often exhibit extreme volatility. For example, during peak periods of renewable energy generation or sudden market regulation, transaction volume may surge in a short period, while during off-peak periods or when the market is stable, it may plummet. With a fixed voting cycle, the traditional PBFT consensus mechanism may struggle to dynamically adapt transaction confirmation efficiency to market demands, thus affecting data storage efficiency and the real-time nature of energy dispatch. Therefore, in this embodiment, the system uses a transaction fluctuation monitoring mechanism to analyze real-time trends in market transaction volume and adjusts the PBFT consensus voting cycle based on transaction load. When transaction volume increases rapidly, the system automatically shortens the PBFT voting cycle, accelerating the consensus process and allowing transaction data to be written to the blockchain more quickly, reducing the risk of transaction backlog. Simultaneously, the system dynamically increases the parallel processing capacity of voting nodes. When the transaction load exceeds a certain threshold (e.g., transaction growth exceeds 50% per unit time), the system automatically implements a load balancing mechanism for voting nodes, enabling multiple nodes to verify transactions simultaneously, thereby improving overall transaction throughput. When transaction volume decreases, the system automatically extends the voting cycle, reducing unnecessary consumption of computing resources, optimizing blockchain storage efficiency, and preventing excessive computing resource consumption even when the energy trading market is under low load. This optimization not only improves the real-time performance of energy transactions but also reduces the storage burden caused by transaction backlogs, ensuring that the blockchain maintains efficient and stable operation in both peak and off-peak scenarios.

[0110] Furthermore, during periods of high transaction volatility, the system employs a dynamic voting weight adjustment mechanism. This mechanism adjusts the voting weights of different nodes in real time based on changes in transaction volume. For instance, when transaction volume is high, the system prioritizes increasing the voting weights of nodes with stronger computing power to improve transaction processing speed. Conversely, when transaction volume is low, the system prioritizes nodes with stronger data storage capabilities to lead the consensus process, ensuring the integrity of transaction data and long-term storage reliability. This approach ensures that the system maintains the optimal consensus strategy under varying market conditions, improving overall storage and transaction efficiency.

[0111] The specific steps for S3 are as follows:

[0112] S3.1: Assess the credibility of the energy data and allocate storage areas based on the credibility.

[0113] Specifically, in the energy internet environment, energy data comes from diverse sources, including but not limited to smart meters, charging piles, power trading platforms, distributed energy systems, and wind-solar hybrid microgrids. Data from different sources has varying degrees of credibility. Therefore, this method first constructs an energy data credibility model. This model comprehensively considers multiple factors such as the reliability of the data source, historical transaction records, data integrity, calibration of the acquisition equipment, data consistency, network communication quality, and smart contract execution, assigning a credibility weight to each piece of energy data. Credibility calculation methods include, but are not limited to, statistical models based on historical records, scoring mechanisms based on transaction behavior analysis, and machine learning models based on artificial intelligence (such as decision trees, random forests, and deep neural networks). Multi-dimensional data analysis methods improve the accuracy of credibility assessment. After the credibility weights are determined, the system stores energy data in different storage areas. Data with high credibility is directly stored in the main blockchain to ensure data immutability, security, and efficient traceability. Data with lower credibility is stored in a secondary blockchain, which allows for further smart contract verification before deciding whether to migrate to the main blockchain or adopt a more flexible storage scheme to reduce storage burden. The advantages of adopting a trustworthiness rating storage strategy are that it avoids low-trustworthiness data directly occupying the main blockchain's storage resources, prevents pollution of the main blockchain data, improves the overall trustworthiness of the blockchain, and provides an upgradeable storage mechanism, allowing data trustworthiness to be dynamically adjusted over time or as the verification process progresses. For example, in the distributed photovoltaic trading market, the trustworthiness of data from some small power generation companies may be lower than that of data from large grid operators. Therefore, when storing data, a trustworthiness rating mechanism can be used to classify and store different types of data, thereby improving the credibility of transaction data and optimizing blockchain storage efficiency.

[0114] S3.2: When the main blockchain and the secondary blockchain interact with each other, perform interactive processing;

[0115] Specifically, to ensure data consistency and security during the interaction between the primary and secondary blockchains, this method employs a multi-layered security authentication mechanism, including but not limited to identity verification, data integrity verification, zero-knowledge proofs (ZKP), physical layer signatures, and time lock verification. First, before data interaction, the data on the secondary blockchain undergoes an identity authentication mechanism to verify whether the data originates from a legitimate and trusted node. Preliminary verification is also performed through smart contracts. For example, in a blockchain consensus network, only data submitted through authorized smart meters or electricity trading platforms is allowed to participate in the primary-secondary blockchain interaction. Second, for data submitted from the secondary blockchain to the primary blockchain, this method uses zero-knowledge proof technology to verify the authenticity and consistency of the data without revealing transaction details, preventing data tampering or forgery. Furthermore, to further enhance data security, this method incorporates physical layer signatures, utilizing the frequency, phase, and waveform characteristics of power line carrier signals as unique identifiers to ensure the authenticity of the data source and guarantee data immutability even during cross-chain transmission. During the interaction, the system employs a time-lock mechanism. For data with low credibility, a secure storage period is required before it enters the main blockchain. During this period, it undergoes review by more verification nodes or smart contract execution. Only after its credibility reaches a set threshold can it be officially migrated to the main blockchain. This method effectively prevents malicious data from rapidly entering the main blockchain and avoids data pollution. Furthermore, to improve interaction efficiency, this method uses an asynchronous cross-chain interaction mechanism. When data exchange occurs between the main and secondary blockchains, the system prioritizes high-priority energy transaction data, such as real-time electricity transactions and key market settlement data. For low-priority data (such as equipment log data and historical load curves), a delayed storage method is used for batch synchronization, thereby optimizing the utilization efficiency of storage resources. For example, in a new energy trading market, the initial transaction data submitted by a distributed photovoltaic power generator might be stored on a secondary blockchain due to a lack of transaction records. As subsequent transactions are successfully completed, the generator's credibility gradually increases. Once the system detects that its credibility has exceeded the main blockchain's storage threshold, it automatically triggers a smart contract to migrate the generator's data to the main blockchain, ensuring the complete storage of credible data and reducing the system's initial storage pressure on low-credibility data. Compared to the traditional blockchain's single storage mechanism, this approach significantly improves data security, storage flexibility, and transaction credibility, providing a more reliable guarantee for the trusted storage and secure trading of energy data.

[0116] The specific steps of S3.1 are as follows:

[0117] S3.1.1: Calculate the credibility weight of each energy transaction in the energy data;

[0118] Specifically, a credibility weight calculation model is established for different types of energy transaction data in the energy internet to quantitatively assess their credibility before data storage. The calculation of credibility weights comprehensively considers multiple dimensions of factors, including but not limited to the credibility of the data source, the reputation of the transacting party, historical transaction records, data integrity, data anomalies, transaction frequency, and data encryption level. For example, the credibility of data from devices such as smart meters, photovoltaic inverters, wind power monitoring systems, and charging piles may be affected by factors such as device operating status, data transmission path, and whether it has passed through an authentication gateway. Therefore, weighted calculations can be performed based on the historical stability of the device and data integrity. Furthermore, if a transaction data source originates from consensus authentication across multiple nodes, such as transactions verified by multiple power trading platforms or third-party certification bodies, its credibility will be relatively high, and a higher weight will be assigned during weight calculation. Conversely, if the transaction data contains anomalies, such as incomplete transactions, frequent order cancellations, or significant discrepancies between the transaction amount and historical patterns in the transacting party's historical records, its credibility weight will be reduced. Credibility scores can be calculated using a weighted scoring model to ensure the scientific validity and rationality of credibility assessments, thereby supporting subsequent tiered storage decisions.

[0119] S3.1.2: Compare the credibility weight with the preset credibility threshold. If it is greater than or equal to the credibility threshold, allocate the energy transaction data to the main blockchain.

[0120] Specifically, based on the calculated credibility weight of energy transaction data, the system compares this weight value with a preset credibility threshold to determine the data storage path. This credibility threshold is pre-set by the system and can be dynamically adjusted based on historical data. Typically, the credibility threshold is set comprehensively based on industry regulatory standards, transaction history stability analysis, and data statistical patterns. For example, for large-scale power grid transactions, the credibility threshold may be higher to ensure that the main blockchain only stores fully verified high-value transaction data. For scenarios such as distributed photovoltaic transactions where data fluctuations may occur, the credibility threshold can be appropriately relaxed to improve data storage flexibility. When the credibility weight of a certain energy transaction data is greater than or equal to the credibility threshold, the system will directly store the data on the main blockchain. The main blockchain uses a more stringent consensus mechanism (such as PBFT or PoS) to ensure the immutability and high security of the data. This process not only ensures that the data stored on the main blockchain is high-credibility data, reducing resource consumption caused by storing unnecessary data, but also improves the overall operating efficiency of the blockchain, enabling high-value transactions to be quickly confirmed through consensus, thus improving transaction transparency and stability. For example, in the settlement process of the electricity spot market, high-value transactions must ensure the authenticity and integrity of the data. Therefore, these transactions are usually highly credible, and the system will prioritize storing them on the main blockchain to ensure the fairness and credibility of energy settlement.

[0121] S3.1.3: If the value is less than the trust threshold, allocate the energy transaction data to the secondary blockchain;

[0122] Specifically, if the credibility weight of energy transaction data is lower than the credibility threshold, the system will store the data on a secondary blockchain for subsequent processing and verification. The secondary blockchain typically employs a lighter-weight storage and consensus mechanism, such as a DAG (Directed Acyclic Graph) structure or sidechain storage, enabling faster storage of large amounts of data while reducing computational resource consumption and improving the overall throughput of the blockchain system. Data stored on the secondary blockchain does not immediately enter the main blockchain but undergoes additional verification steps, including time-lock verification, historical data matching, multi-party consensus signatures, and AI anomaly detection for further review. For example, if the credibility of a certain new energy transaction data is lower than the credibility threshold, but its data matches historical patterns well, the system may automatically increase its credibility weight after a period of stable verification and eventually migrate it to the main blockchain for storage. Conversely, if the data shows anomalies during subsequent monitoring (such as multiple transaction nodes repeatedly submitting data but the transaction not being completed), an anomaly warning may be triggered, and manual review may be required. This layered storage mechanism effectively reduces the burden on the main blockchain while ensuring data integrity and security, making it particularly suitable for large-scale data transactions and storage in energy internet scenarios. For example, in the distributed energy trading market, since the trading behavior of small photovoltaic users may be more uncertain, the storage method of the secondary blockchain can provide a flexible verification mechanism for this data, ensuring that high-credibility data can eventually enter the main blockchain, while low-credibility or abnormal transactions can be effectively screened, thereby improving the security and stability of the overall system.

[0123] The specific steps of S3.2 are as follows:

[0124] S3.2.1: Verify the identity of data on the secondary blockchain, and then retrieve data from the main blockchain after successful authentication;

[0125] In this embodiment, in order to ensure the legitimacy and traceability of data in the secondary blockchain during the interaction process, an authentication mechanism based on digital signatures and physical layer feature binding is adopted.

[0126] Specifically, the system first extracts key attributes such as transaction hashes, signature information, and data source IDs from the data to be interacted with in the secondary blockchain. It then verifies the data signature using Public Key Infrastructure (PKI) or Distributed Identity Authentication (DID) mechanisms. If the signature is valid, the data source is considered trustworthy; otherwise, the data interaction request is rejected. Furthermore, the system combines the physical layer signature of the power line carrier signal with a comparison of physical layer signal characteristics (such as phase offset, modulation mode, and voltage / current waveforms) to verify whether the data truly originates from a specific physical device, preventing man-in-the-middle attacks or forged data sources. Only after successful authentication will the system allow the data to access relevant data in the main blockchain and execute the next operation. The advantage of this authentication mechanism is that it not only guarantees the authenticity of the data source at the encryption level but also further enhances security from a physical perspective, ensuring that only legitimate data can enter the cross-chain interaction process and preventing malicious data from polluting the blockchain storage.

[0127] S3.2.2: Encrypt data in the main blockchain and secondary blockchain using zero-knowledge proofs;

[0128] Specifically, the system first performs hash calculations on the data that needs to be exchanged between the main blockchain and the secondary blockchain to generate a unique hash fingerprint. Then, it constructs a data verification scheme based on zk-SNARKs (Simple Non-Interactive Zero-Knowledge Proofs) or zk-STARKs (Scalable Transparent Zero-Knowledge Proofs). This scheme allows verification nodes between the main and secondary blockchains to prove data consistency across both chains without exposing the specific data content. For example, in an energy trading data migration scenario, the data provider on the secondary blockchain can use a zero-knowledge proof algorithm to calculate the transaction hash and send it to the verification node on the main blockchain. The verification node then uses the same algorithm to calculate the corresponding transaction hash on the main blockchain. If the two match, the data consistency is proven, thus completing verification without directly exposing the transaction content. The advantage of this technology is that it avoids the privacy risks associated with plaintext data transmission in traditional blockchain cross-chain interactions, while also effectively preventing data tampering and improving the security of data interaction. Furthermore, zero-knowledge proofs can also be used for identity privacy protection, ensuring that even if the same transaction is stored differently on different chains, the data's validity can still be verified, significantly improving the security and privacy protection capabilities of data sharing.

[0129] S3.2.3: Update the credibility weight of the data involved in the transaction based on the interaction results, and determine whether the data transfer conditions are met;

[0130] Specifically, the Trust Score is calculated based on multiple factors, including but not limited to the historical reputation of the data source, the success rate of smart contract execution, data integrity verification, and the identity authentication status of the transacting parties. In this embodiment, the system adopts a trust score calculation method based on a Bayesian update model. When a transaction's data successfully passes cross-chain interaction and no anomalies occur during the main blockchain's verification process (such as data conflicts or invalid signatures), the trust score of that data will increase. If anomalies are detected during the interaction process (such as zero-knowledge proof mismatches, identity verification failures, or transaction hash conflicts), the trust score will decrease, triggering a security review mechanism. Furthermore, to ensure the reasonableness of the trust score, the system also uses machine learning-based anomaly detection models (such as LSTM or random forests) to analyze historical transaction data to identify whether data fraud or malicious tampering exists. For example, in a new energy transaction scenario, if data from a distributed photovoltaic power station has been repeatedly rejected for storage on the main blockchain due to anomalies, the trust score of its future submitted transaction data may decrease. The system will prioritize retaining this data in the secondary blockchain and require additional verification proof. This dynamic trust adjustment mechanism ensures that only stable and reliable data will ultimately be stored on the main blockchain, thereby optimizing storage resources and improving the overall security and data reliability of the system.

[0131] S3.2.4: If the data transfer conditions are met, perform a second verification using a time lock. Once the verification is successful, proceed with the data transfer.

[0132] Specifically, time locks can be based on block height or smart contracts to ensure that data, even after meeting migration conditions, still requires a buffer period before being written to the main blockchain. For example, in the electricity trading market, a new energy power generation transaction record may reach the trustworthiness migration threshold on a secondary blockchain. However, before migrating to the main blockchain, the system will enforce a time lock mechanism, setting a minimum time window (such as 50 block confirmation times or 1 hour). During this period, other verification nodes can submit challenges. If the transaction is not challenged during the time lock period, the system will perform the final storage operation. Furthermore, to prevent malicious manipulation of the time lock, the system will also implement a multi-signature mechanism, meaning the final decision on data migration requires consensus approval from multiple trusted nodes. The advantage of this time lock mechanism is that it effectively prevents short-term data tampering. Even if some low-trust data eventually passes the trustworthiness assessment, it still needs a certain observation period to ensure the data's authenticity, thereby improving the storage quality and security of the main blockchain.

[0133] The authentication of data on the secondary blockchain includes:

[0134] A physical layer signature is generated based on the power line carrier communication characteristics corresponding to the data in the secondary blockchain.

[0135] The main blockchain receives and verifies the physical layer signature.

[0136] Example 2:

[0137] Please see Figure 2 The present invention provides an embodiment of an energy internet data security transmission and authentication system, the data security transmission and authentication system comprising a data acquisition module, a data storage module and a data protection module;

[0138] The data acquisition module is used to collect energy data from the energy internet;

[0139] The data storage module is configured with a block configuration strategy, which is used to adjust the block size and storage method according to the characteristics and flow changes of energy data.

[0140] The data protection module is used to provide data encryption and security authentication mechanisms.

[0141] The data storage module includes:

[0142] A storage allocation unit is used to perform a credibility rating on the energy data and allocate storage areas according to the credibility.

[0143] A capacity adjustment unit is used to adjust the capacity of the storage area based on the flow fluctuations of the energy data.

[0144] The block configuration strategy includes a trustworthiness assessment logic and a block adjustment logic. The trustworthiness assessment logic is configured within the storage allocation unit, and the block adjustment logic is configured within the capacity adjustment unit.

[0145] The credibility assessment logic includes:

[0146] Calculate the credibility weight of each energy transaction in the energy data;

[0147] The credibility weight is compared with a preset credibility threshold. If it is greater than or equal to the credibility threshold, the energy transaction data is allocated to the main blockchain.

[0148] If the value is less than the trust threshold, the energy transaction data will be allocated to the secondary blockchain.

[0149] The block adjustment logic includes:

[0150] Predicting data flow using time-series forecasting models;

[0151] When an increase in energy data traffic is predicted, the storage capacity is expanded according to the predicted amount.

[0152] When a decrease in energy data traffic is predicted, the capacity of the storage area is reduced based on the predicted amount.

[0153] The data storage module further includes:

[0154] A data interaction unit, which provides data interaction logic between the main blockchain and the secondary blockchain;

[0155] The supply and demand adjustment unit is used to monitor the supply and demand balance of the energy internet. When the supply and demand balance is imbalanced, the block capacity in the main blockchain is adjusted.

[0156] The data interaction logic includes:

[0157] The data on the secondary blockchain is authenticated, and the data on the main blockchain is retrieved after successful authentication.

[0158] Encrypting data in the main blockchain and secondary blockchain using zero-knowledge proofs;

[0159] Based on the interaction results, the credibility weight of the data involved in the transaction is updated to determine whether the data transfer conditions are met.

[0160] If the data transfer conditions are met, a second verification is performed using a time lock. Once the verification is successful, the data transfer is initiated.

[0161] The authentication of data on the secondary blockchain includes:

[0162] A physical layer signature is generated based on the power line carrier communication characteristics corresponding to the data in the secondary blockchain.

[0163] The main blockchain receives and verifies the physical layer signature.

[0164] The adjustment of block size in the main blockchain includes:

[0165] The trust threshold is adjusted according to the emergency consensus mechanism to reduce the data writing frequency of the main blockchain;

[0166] Based on the direction of imbalance, energy data in the main blockchain is prioritized and the low-priority energy data is stored in the secondary blockchain.

[0167] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A data security transmission and authentication system applied to energy internet data, characterized in that, The data security transmission and authentication system comprises a data acquisition module, a data storage module and a data protection module, wherein: The data acquisition module is configured to acquire energy data of the energy internet; The data storage module is configured with a block configuration strategy, which is configured to adjust the size and storage mode of the block according to the characteristics and flow changes of the energy data; The data protection module is configured to provide data encryption and security authentication mechanism; The data storage module comprises: A storage allocation unit configured to perform credibility rating on the energy data and allocate storage areas according to the credibility; A capacity adjustment unit configured to adjust the capacity of the storage area according to the flow fluctuation of the energy data; The block configuration strategy comprises a credibility evaluation logic and a block adjustment logic, wherein the credibility evaluation logic is configured in the storage allocation unit, and the block adjustment logic is configured in the capacity adjustment unit; The credibility evaluation logic comprises: Calculating the credibility weight of each energy transaction data in the energy data; Comparing the credibility weight with a preset credibility threshold, if greater than or equal to the credibility threshold, allocating the energy transaction data to the main block chain; If less than the credibility threshold, allocating the energy transaction data to the auxiliary block chain; The block adjustment logic comprises: Predicting the data flow by a time series prediction model; When predicting an increase in energy data flow, expanding the capacity of the storage area according to the prediction; When predicting a decrease in energy data flow, reducing the capacity of the storage area according to the prediction.

2. The data security transmission and authentication system of claim 1, wherein, The data storage module further comprises: A data interaction unit configured to provide data interaction logic between the main block chain and the auxiliary block chain; A supply and demand adjustment unit configured to monitor the supply and demand balance state of the energy internet, and adjust the block capacity in the main block chain when the supply and demand balance state is unbalanced.

3. The data security transmission and authentication system of claim 2, wherein, The data interaction logic comprises: Verifying the data of the auxiliary block chain, and calling the data of the main block chain after verification; Encrypting the data of the main block chain and the auxiliary block chain by zero-knowledge proof; Updating the credibility weight of the data of the transaction according to the interaction result, and judging whether the data transfer condition is met; If the data transfer condition is met, performing secondary verification by time lock, and transferring the data after verification.

4. The data security transmission and authentication system of claim 3, wherein, The data verification of the auxiliary block chain comprises: Generating a physical layer signature according to the power carrier communication characteristics corresponding to the data of the auxiliary block chain; The main block chain receives and verifies the physical layer signature.

5. The data security transmission and authentication system of claim 3, wherein, The adjustment of the block capacity in the main block chain comprises: Adjusting the credibility threshold according to the emergency consensus mechanism, and reducing the data writing frequency of the main block chain; According to the imbalance direction, the energy data in the main block chain is classified according to priority, and the screened energy data is stored in the auxiliary block chain, wherein the energy data is screened according to the size of the priority.

6. The data security transmission and authentication system of claim 5, wherein, The priority classification of the energy data in the main block chain according to the imbalance direction comprises: Adjusting the consensus algorithm parameters of the main block chain according to the direction and severity of the supply and demand imbalance; According to the consensus algorithm parameter, the priority of the energy data in the main blockchain is adjusted.

7. The data secure transmission and authentication system of claim 6, wherein, The consensus algorithm parameter of the main blockchain is adjusted, including: When the energy supply cannot meet the demand, increase the staking weight of the proof-of-stake node, and increase the decision weight of the reputation node in the consensus process; When the market transaction volatility is greater than the set volatility threshold, shorten the voting period of the Byzantine fault tolerance.

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