Electric power information security storage supervision system based on block chain

Abstract

The invention discloses an electric power information security storage supervision system based on a block chain. A multi-head nerve attention mechanism and a fuzzy entropy technology are introduced into an alarm information processing module. By preprocessing the power equipment alarm information collected in real time, the system can extract time sequence features, environmental parameters and historical record information, and the complexity and uncertainty of each piece of alarm information are quantified by using fuzzy entropy calculation. In combination with a multi-head nerve attention mechanism, the system can intelligently identify alarm information possibly causing major problems and preferentially distribute processing resources. Compared with a traditional alarm processing system, the alarm information processing efficiency is improved, meanwhile, the situations of false alarm and missing alarm are effectively reduced, it is ensured that key alarm information can be responded in time, and therefore the safety and stability of an electric system are greatly improved.

Description

Technical Field

[0001] The present invention belongs to the field of power information security storage and supervision system, and specifically, relates to a power information security storage and supervision system based on blockchain. Background Art

[0002] With the rapid development of power systems and the deepening of intelligent applications, the real-time collection, transmission and storage of power data have become increasingly important. Traditional power information systems usually rely on centralized storage and management methods, but this method has limitations when processing large amounts of real-time data. Although the existing blockchain technology is very mature, due to the huge amount of power data, many systems have turned to distributed storage environments. However, in a distributed storage environment, data is distributed on multiple nodes. How to verify the validity of data while ensuring data security has become a technical bottleneck for traditional methods.

[0003] Existing verification methods usually rely on public verification or exposure of partial data, which increases the risk of data leakage to a certain extent and is difficult to meet the needs of high security. In addition, the alarm information in the power system is not only huge in quantity, but also involves complex environmental parameters, equipment status, historical records and other factors. Traditional alarm processing systems lack intelligent analysis capabilities and are difficult to effectively identify potential major risks, often leading to false alarms, missed alarms, and even ignoring some alarm information that may cause major accidents. When faced with multi-source alarm information, it is difficult for the system to achieve efficient alarm sorting and priority processing, resulting in untimely response to key alarm information, endangering the stable operation of the power system.

[0004] In view of this, the present invention is proposed. Summary of the invention

[0005] The technical problem to be solved by the present invention is to overcome the shortcomings of the prior art and provide a blockchain-based power information security storage and supervision system to solve the problems raised in the above-mentioned background technology.

[0006] In order to solve the above technical problems, the basic concept of the technical solution adopted by the present invention is:

[0007] A blockchain-based power information security storage and supervision system, comprising: a data acquisition module, a delay-tolerant network (DTN) module, a data slicing and encryption module, a blockchain network module, a zero-knowledge proof (ZKP) module, an alarm information processing module, and a storage node management unit;

[0008] A data acquisition module, used for collecting power data from the power equipment in real time and transmitting the data to the delay tolerant network module;

[0009] A delay tolerant network module, used for prioritizing the power data according to the time sensitivity of the data, and transmitting the classified data to the data slicing and encryption module;

[0010] A data slicing and encryption module is used to slice the priority-classified power data into multiple data blocks, and encrypt each data block to generate an encrypted data block;

[0011] A blockchain network module, which is used to receive encrypted data blocks and distribute the encrypted data blocks to storage nodes of multiple substations for distributed storage through blockchain technology. The blockchain network implements distributed storage and management of data and records the storage path of data blocks based on Hyperledger Fabric;

[0012] A zero-knowledge proof module is used to verify the validity of the encrypted data block based on the zero-knowledge proof protocol in the blockchain network to ensure the confidentiality and integrity of the data in distributed storage;

[0013] The alarm information processing module is used to collect and store alarm information related to power data. By analyzing historical alarm data, environmental parameters, equipment status and other information, it uses the multi-head neural attention mechanism and fuzzy entropy technology to predict possible risks in the future. Finally, it dynamically adjusts the weight of the alarm information, identifies those alarms that may cause major problems in the future, and prioritizes the allocation of processing resources for this information.

[0014] The storage node management unit is used to manage and maintain the storage nodes distributed in multiple substations to ensure the security and traceability of the distributed storage of encrypted data blocks.

[0015] Optionally, when the data is prioritized according to the time sensitivity of the power data, a time sensitivity analysis is performed on the collected power data to obtain the sensitivity level of the power data, and the power data is classified according to preset priority rules based on the time sensitivity analysis, and the classified data is transmitted to the data slicing and encryption modules respectively according to the priority of the data.

[0016] Optionally, the sensitivity levels include high sensitivity, medium sensitivity and low sensitivity, wherein high sensitivity data is assigned the highest priority, and medium sensitivity and low sensitivity data correspond to medium priority and low priority respectively.

[0017] Optionally, a slicing strategy needs to be preset before data slicing. After obtaining the power information data, the received power data is sliced ​​into multiple independent data blocks according to the preset slicing strategy. After each execution of the data slicing strategy, the system records the details of the slicing strategy and the execution results on the blockchain;

[0018] After obtaining independent data blocks, an asymmetric encryption algorithm is used to encrypt each data block after segmentation, and all data blocks are converted into encrypted data blocks. Each encrypted data block is accompanied by a unique encryption identifier. Finally, the generated encrypted data blocks will be transmitted to each storage node in the blockchain network for distributed storage.

[0019] Optionally, before uploading data to the blockchain network, each substation storage node needs to be configured with a Hyperledger Fabric node. When the blockchain network module receives an encrypted data block containing metadata, the system uses a node selection algorithm to select a suitable node cluster from the available substation storage nodes based on the current node load, network bandwidth, storage capacity, and geographic location.

[0020] The blockchain network generates a data block distribution instruction, instructing the distribution of encrypted data blocks to the selected storage nodes. After the distribution is completed, the blockchain network records the storage path, storage node identifier and related metadata of each encrypted data block in the distributed ledger of the blockchain.

[0021] Optionally, during the distribution process, the blockchain network will dynamically select substation storage nodes suitable for storing these data blocks based on the metadata of the encrypted data blocks and the current state of the blockchain network, and allocate high-priority data blocks to nodes with better performance. The system will also periodically verify the paths of the stored data blocks through the blockchain network and check their storage status at each node. If an abnormality is detected, the blockchain network will automatically reallocate the storage node.

[0022] Optionally, after the encrypted data block is generated, the node that owns the data block calculates a hierarchical zero-knowledge proof through the generator. Different levels of proof complexity are selected according to the sensitivity and purpose of the data block. For highly sensitive data blocks, a multi-level proof mechanism is adopted, each layer provides additional security verification, and uses a quantum-resistant encryption algorithm to generate a label commitment σ i , verification factor τ i , whose expression is: Among them, ε lattice is a lattice-based encryption function used to generate quantum-secure label commitments, M i is the data message or its summary at level i, r i is a random number used to ensure the randomness and security of the encryption process, σ i The tag commitment generated for level i is the encrypted message M i and a random number r i The hash value of V lattice is a verification function based on lattice theory, used to generate quantum-safe verification factors, K iis the shared key or verification parameter of the i-th level, τ i The verification factor generated for level i is the hash of the tag commitment and the verification parameter, is a hash function used to generate a secure hash value;

[0023] After obtaining the hierarchical zero-knowledge proof, it is distributed to each storage node in the blockchain network together with the corresponding encrypted data block. After receiving the encrypted data block and its hierarchical zero-knowledge proof, the storage node starts the verification process. The validator node uses the shared verification parameters to independently verify each level of proof according to the hierarchical zero-knowledge proof protocol. The expression is: Among them, V layered It is the verification result of the layered zero-knowledge proof. If all layers are verified, the value is true. is the verification function of the i-th level, responsible for verifying whether the label commitment and verification factor of this level are correct, σ i , τ i is the label commitment and verification factor of the i-th level, K i is the shared key or authentication parameter of the i-th level;

[0024] If all levels of verification pass, the data block will be officially stored in the distributed storage system, and the verification results and related information will be recorded in the distributed ledger of the blockchain. If the verification fails, the storage node will refuse to store the data block and will not add it to the distributed storage of the system. It will immediately issue an alarm, notify and send the alarm information to the alarm information processing module.

[0025] Optionally, the alarm information processing module performs the following steps during priority processing:

[0026] The alarm information processing module collects the alarm information of power equipment in real time, pre-processes the data, extracts time series characteristics, environmental parameters and historical alarm record information, and performs fuzzy entropy on the time series data of the alarm information. Calculate and obtain the complexity and uncertainty index of each alarm information, fuzzy entropy The expression to be calculated is: in, represents a time series segment of length m, r is the fuzziness of the sequence, n represents the number of data points in the sequence, r represents the allowed fuzziness threshold, which is used to control the similarity between two sequence segments, N is the total number of data points, is the fuzziness function, and the sequence fragment X is calculated i and similarities between;

[0027] Use historical data to build and train a multi-head neural attention model, input the complexity and uncertainty indicators calculated by fuzzy entropy and other alarm features into the multi-head neural attention model. Different attention heads of the multi-head neural attention model focus on different features, and independently learn the importance and relevance of these features based on the output results of fuzzy entropy.

[0028] According to the results calculated by fuzzy entropy and multi-head neural attention mechanism, the weight of the alarm information is dynamically adjusted to optimize the alarm sorting. The expression is: W alert is the dynamic weight of the warning information, α i is the weight coefficient of the i-th attention head, calculated by the multi-head attention mechanism, β is the weight coefficient of the fuzzy entropy, which is used to balance the influence of the fuzzy entropy in the final weight;

[0029] The alarm information processing module processes the alarm information according to the optimized sorting, gives priority to responding to alarms that may cause major problems and continuously monitors the processing effect of the alarm information. It regularly re-evaluates the stored alarm information through fuzzy entropy technology. If new anomalies or risks are detected, the alarm information processing module will dynamically adjust the alarm processing strategy and trigger the necessary emergency response.

[0030] After adopting the above technical solution, the present invention has the following beneficial effects compared with the prior art. Of course, any product implementing the present invention does not necessarily need to achieve all the advantages described below at the same time:

[0031] 1. The alarm information processing module of the present invention introduces a multi-head neural attention mechanism and fuzzy entropy technology. By pre-processing the real-time collected alarm information of power equipment, the system can extract time series characteristics, environmental parameters and historical record information, and use fuzzy entropy calculation to quantify the complexity and uncertainty of each alarm information. Combined with the multi-head neural attention mechanism, the system can intelligently identify alarm information that may cause major problems and prioritize the allocation of processing resources. Compared with traditional alarm processing systems, the present invention improves the efficiency of alarm information processing while effectively reducing false alarms and missed alarms, ensuring that key alarm information can be responded to in a timely manner, thereby greatly improving the safety and stability of the power system.

[0032] 2. By combining zero-knowledge proof technology with blockchain technology, the system can achieve efficient data management and secure storage in a distributed storage environment. The storage path and verification results of each encrypted data block are recorded in the distributed ledger of the blockchain, ensuring the data is tamper-proof and traceable. When a storage node is abnormal or fails, the system can quickly reallocate the storage node to ensure the continuous availability and security of the data.

[0033] The specific implementation modes of the present invention are further described in detail below in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The drawings described below are only some embodiments. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0035] In the figure:

[0036] Figure 1 Block diagram of the structural security storage supervision system.

[0037] It should be noted that these drawings and textual descriptions are not intended to limit the conceptual scope of the present invention in any way, but are intended to illustrate the concept of the present invention for those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0038] The present invention will now be described in further detail with reference to the accompanying drawings.

[0039] See also Figure 1 As shown, in this embodiment, a blockchain-based power information security storage and supervision system is provided, including a data acquisition module, a delay-tolerant network (DTN) module, a data slicing and encryption module, a blockchain network module, a zero-knowledge proof (ZKP) module, an alarm information processing module, and a storage node management unit;

[0040] A data acquisition module, used for collecting power data from the power equipment in real time and transmitting the data to the delay tolerant network module;

[0041] A delay tolerant network module, used for prioritizing the power data according to the time sensitivity of the data, and transmitting the classified data to the data slicing and encryption module;

[0042] A data slicing and encryption module is used to slice the priority-classified power data into multiple data blocks, and encrypt each data block to generate an encrypted data block;

[0043] The blockchain network module is used to receive encrypted data blocks and distribute the encrypted data blocks to storage nodes of multiple substations for distributed storage through blockchain technology. The blockchain network implements distributed storage and management of data and records the storage path of data blocks based on Hyperledger Fabric. The blockchain network module implements distributed storage and management of data through Hyperledger Fabric technology, ensuring the immutability and traceability of data. The storage path of the data is also recorded in the blockchain for later auditing and verification.

[0044] The zero-knowledge proof module is used to verify the validity of the encrypted data block in the blockchain network based on the zero-knowledge proof protocol to ensure the confidentiality and integrity of the data in the distributed storage; the zero-knowledge proof module ensures the confidentiality and integrity of the data in the distributed storage, and the specific content of the data does not need to be exposed during the verification process, which protects data privacy while also improving the security of the system. In the power trading system, zero-knowledge proof can be used to verify the legitimacy of the transaction without disclosing the specific details of the transaction, thereby protecting the commercial secrets of the participants.

[0045] The alarm information processing module is used to collect and store alarm information related to power data. By analyzing historical alarm data, environmental parameters, equipment status and other information, the multi-head neural attention mechanism and fuzzy entropy technology are used to predict possible risks in the future. Finally, the weight of the alarm information is dynamically adjusted to identify those alarms that may cause major problems in the future, and prioritize the allocation of processing resources for this information. The alarm information processing module can not only analyze the current alarm information, but also predict possible risks in the future and prioritize high-risk alarms through the multi-head neural attention mechanism and fuzzy entropy technology. This greatly improves the system's predictability and emergency response capabilities. By analyzing previous equipment failure records and current operating status, the system can predict that a certain device may be about to fail, and arrange maintenance in advance to avoid power outages.

[0046] The storage node management unit is used to manage and maintain the storage nodes distributed in multiple substations to ensure the security and traceability of the distributed storage of encrypted data blocks.

[0047] In this embodiment, when the data is prioritized according to the time sensitivity of the power data, the collected power data is analyzed for time sensitivity to obtain the sensitivity level of the power data, and the power data is classified according to the preset priority rules based on the time sensitivity analysis, and the classified data is transmitted to the data slice and encryption module respectively according to the priority of the data. By analyzing the time sensitivity of the power data and classifying it, the system can reasonably allocate resources according to the importance and timeliness of the data to ensure that key data is given priority. This method improves the response speed of the system to high-priority data and also optimizes the utilization efficiency of resources. The sensitivity levels include high sensitivity, medium sensitivity and low sensitivity, among which high-sensitivity data is given the highest priority, and medium sensitivity and low sensitivity data correspond to medium priority and low priority, respectively. The introduction of sensitivity levels and priority rules enables the system to flexibly adjust the processing strategy according to the characteristics of different data. For example, when resources are limited, the system can give priority to high-sensitivity data and delay the processing of low-sensitivity data. This flexibility can help the system better adapt to the changing power environment and real-time requirements.

[0048] For example, in a smart grid, suppose a substation in a certain area detects an abnormality in the equipment, and the acquisition module obtains the relevant power data in real time. The time sensitivity of this data is very high, because any delay may lead to a larger power failure or even a power outage. The system performs a time sensitivity analysis on this data, determines it as highly sensitive data, and gives it the highest priority. Subsequently, this data is preferentially transmitted to the data slicing and encryption module, processed quickly and stored in the most secure storage node for subsequent analysis and troubleshooting. This priority processing mechanism ensures that measures are taken in the shortest time to avoid large-scale power failures.

[0049] In this embodiment, a slicing strategy needs to be preset before data slicing. After the power information data is acquired, the received power data is sliced ​​into multiple independent data blocks according to the preset slicing strategy. After each execution of the data slicing strategy, the system records the details of the slicing strategy and the execution results on the blockchain. Presetting the slicing strategy before data slicing and recording the execution details on the blockchain enhances the transparency and traceability of the entire data processing process.

[0050] After obtaining the independent data blocks, an asymmetric encryption algorithm is used to encrypt each data block after segmentation, and all data blocks are converted into encrypted data blocks. Each encrypted data block is accompanied by a unique encryption identifier. Finally, the generated encrypted data blocks will be transmitted to each storage node in the blockchain network for distributed storage. After the data is sliced, an asymmetric encryption algorithm is used to encrypt each data block, and a unique encryption identifier is assigned to each encrypted data block. This mechanism ensures the security of data during transmission and storage. Even if a data block is intercepted or tampered with, the attacker cannot decrypt or tamper with the data, thereby protecting the integrity and confidentiality of the data.

[0051] Before uploading data to the blockchain network, each substation storage node needs to be configured with a Hyperledger Fabric node. When the blockchain network module receives an encrypted data block containing metadata, the system uses a node selection algorithm to select a suitable node cluster from the available substation storage nodes based on the current node load, network bandwidth, storage capacity, and geographic location. The expression is: Among them, S * is the node cluster finally selected, S i represents the i-th candidate node, S is the set of all currently available substation storage nodes, and L i For node S i The current load, expressed as CPU usage or I / O load, C i For node S iThe available storage capacity, ι is the weight factor used to adjust the impact of the load-to-storage capacity ratio in the overall formula, μ, v are nonlinear adjustment parameters used to adjust the relative importance of load and storage capacity B i For node S i The network bandwidth, δ is the weight factor used to adjust the impact of network bandwidth in the overall formula, and η is the nonlinear adjustment parameter used to adjust the impact of network bandwidth G i is the geographical weighting factor, representing the node S i The geographical importance of the region may be based on certain strategic requirements. ζ is a nonlinear adjustment parameter used to adjust the impact of the geographical weighting factor. i，j For node S i The physical distance between the data source j and the source j, κ is the attenuation constant of the distance effect, which is used to control the influence of distance on the selection, and λ is the weight factor, which is used to adjust the proportion of geographical location and distance influence in the overall formula;

[0052] The blockchain network generates a data block distribution instruction, instructing the distribution of encrypted data blocks to the selected storage nodes. After the distribution is completed, the blockchain network records the storage path, storage node identifier and related metadata of each encrypted data block in the distributed ledger of the blockchain.

[0053] For example: In a smart grid system, a large amount of power data is collected from multiple substations and sensors every day. This data needs to be segmented, encrypted, and distributedly stored in nodes of multiple substations to ensure the security and reliability of the data. Through the preset segmentation strategy, the system segments the power data into multiple small blocks and encrypts them using an asymmetric encryption algorithm. The encrypted data blocks are assigned unique identifiers, and the optimal storage nodes are selected through the node selection algorithm in the blockchain network. The details of each segmentation and encryption operation, including the storage path and node information of the data block, are recorded in the distributed ledger of the blockchain. In this way, even if an error occurs during data transmission or a node fails, the system administrator can track the flow path of the data block through the blockchain ledger and take corresponding measures to recover or retransmit the data to ensure the security of power data and the reliability of the system.

[0054] In this embodiment, during the distribution process, the blockchain network will dynamically select substation storage nodes suitable for storing these data blocks based on the metadata of the encrypted data blocks and the current state of the blockchain network, and allocate high-priority data blocks to nodes with better performance. The system will periodically verify the paths of the stored data blocks through the blockchain network and check their storage status at each node. If an abnormality is detected, the blockchain network will automatically reallocate the storage nodes. By dynamically selecting storage nodes based on the metadata of the encrypted data blocks and the state of the blockchain network, the system can intelligently allocate high-priority data to nodes with better performance. This intelligent allocation mechanism ensures that key data is processed and stored first, improving the efficiency of data processing and the response speed of the system.

[0055] For example: In the smart grid system, some data (such as load dispatch instructions, fault alarms, etc.) are critical to the stable operation of the power grid. These data usually have high priority and require fast storage and retrieval. Based on the system design, when these high-priority data blocks are generated, the blockchain network will allocate them to storage nodes with higher performance according to the priority of the data and the current network load. These nodes may have faster processing speeds, larger storage capacity, and lower network latency, thereby ensuring timely processing and storage of critical data. After the data is stored, the system regularly performs path verification through the blockchain network to check the storage status of these data blocks in each node. For example, if the storage status of a node is unstable, it may cause data unavailability or loss. At this time, after the system detects the anomaly, it will automatically reallocate these critical data blocks to other healthy nodes to ensure that the data is always in a safe and available state. This dynamic adjustment mechanism greatly improves the security and reliability of power grid data management.

[0056] In this embodiment, after the encrypted data block is generated, the node that owns the data block calculates a hierarchical zero-knowledge proof through the generator. Different levels of proof complexity are selected according to the sensitivity and purpose of the data block. For highly sensitive data blocks, a multi-level proof mechanism is adopted, each layer provides additional security verification, and a quantum-resistant encryption algorithm is used to generate a label commitment σ i , verification factor τ i , whose expression is: Among them, ε lattice is a lattice-based encryption function used to generate quantum-secure label commitments, M i is the data message or its summary at level i, r i is a random number used to ensure the randomness and security of the encryption process, σ i The tag commitment generated for level i is the encrypted message M i and a random number r i The hash value of Vlattice is a verification function based on lattice theory, used to generate quantum-safe verification factors, K i is the shared key or verification parameter of the i-th level, τ i The verification factor generated for level i is the hash of the tag commitment and the verification parameter, It is a hash function used to generate secure hash values. By introducing a layered zero-knowledge proof mechanism and a quantum-resistant encryption algorithm, the system provides multiple security verifications at different levels. This multi-level verification mechanism ensures that even if the security of one level is threatened, other levels can still provide protection, thereby improving the overall data security.

[0057] After obtaining the hierarchical zero-knowledge proof, it is distributed to each storage node in the blockchain network together with the corresponding encrypted data block. After receiving the encrypted data block and its hierarchical zero-knowledge proof, the storage node starts the verification process. The validator node uses the shared verification parameters to independently verify each level of proof according to the hierarchical zero-knowledge proof protocol. The expression is: Among them, V layered It is the verification result of the layered zero-knowledge proof. If all layers are verified, the value is true. is the verification function of the i-th level, responsible for verifying whether the label commitment and verification factor of this level are correct, σ i , τ i is the label commitment and verification factor of the i-th level, K i is the shared key or verification parameter of the i-th level; through the hierarchical verification mechanism, only data blocks that pass all verifications will be officially stored in the distributed storage system, which greatly improves the reliability and integrity of the stored data. The system refuses to store data blocks that have not passed verification, preventing incomplete or tampered data from entering the system.

[0058] If all levels of verification pass, the data block will be officially stored in the distributed storage system, and the verification results and related information will be recorded in the distributed ledger of the blockchain. If the verification fails, the storage node will refuse to store the data block and will not add it to the distributed storage of the system. It will immediately issue an alarm, notify and send the alarm information to the alarm information processing module.

[0059] For example: suppose the system collects a set of highly sensitive power load data, which needs to be distributed and stored in nodes of multiple substations. The system first generates encrypted data blocks for these data blocks and generates multi-level zero-knowledge proofs based on the sensitivity of the data blocks. Each proof level uses different encryption algorithms and verification mechanisms, especially introducing quantum-resistant encryption algorithms to defend against future quantum computing attacks. After generating zero-knowledge proofs, the system distributes the data blocks and corresponding proofs to storage nodes in the blockchain network. After receiving these data, the storage nodes start the hierarchical verification process. By verifying the label commitments and verification factors layer by layer, the system ensures the integrity and authenticity of each data block. If all levels of verification pass, the data block will be formally stored in the distributed storage system and recorded in the blockchain ledger. If the verification fails, for example, the label commitments at a certain level do not match, the system will refuse to store the data block and immediately issue an alarm to notify the administrator to check the problem. The system will also record the alarm information and the data blocks that failed the verification in the blockchain ledger to ensure that the administrator can trace back and analyze the reasons for the failure to prevent similar problems from happening again.

[0060] In this embodiment, the alarm information processing module performs the following steps during priority processing:

[0061] The alarm information processing module collects the alarm information of power equipment in real time, pre-processes the data, extracts time series characteristics, environmental parameters and historical alarm record information, and performs fuzzy entropy on the time series data of the alarm information. Calculate and obtain the complexity and uncertainty index of each alarm information, fuzzy entropy The expression to be calculated is: in, represents a time series segment of length m, r is the fuzziness of the sequence, n represents the number of data points in the sequence, r represents the allowed fuzziness threshold, which is used to control the similarity between two sequence segments, N is the total number of data points, is the fuzziness function, and the sequence fragment X is calculated i and By calculating the fuzzy entropy of the alarm information, the system can quantify the complexity and uncertainty index of each alarm information. This quantitative evaluation can help the system more accurately determine which alarm information needs to be processed first and avoid potential risks caused by ignoring complex alarms.

[0062] The multi-head neural attention model is established and trained using historical data. The complexity and uncertainty indicators calculated by fuzzy entropy and other alarm features are input into the multi-head neural attention model. Different attention heads of the multi-head neural attention model focus on different features, and independently learn the importance and relevance of these features based on the output results of fuzzy entropy. The multi-head neural attention model intelligently sorts the alarm information according to the weights of different features. The system not only pays attention to the current status of the alarm information, but also dynamically adjusts the weight of the alarm information through historical data and the calculation results of fuzzy entropy to ensure that alarms that may cause major problems are handled first.

[0063] According to the results calculated by fuzzy entropy and multi-head neural attention mechanism, the weight of the alarm information is dynamically adjusted to optimize the alarm sorting. The expression is: W alert is the dynamic weight of the warning information, α i is the weight coefficient of the ith attention head, calculated by the multi-head attention mechanism, and β is the weight coefficient of the fuzzy entropy, which is used to balance the influence of the fuzzy entropy in the final weight; the alarm information processing module can not only process the current alarm in real time, but also dynamically adjust the alarm processing strategy through continuous learning of fuzzy entropy technology and multi-head neural attention model. The system regularly re-evaluates the stored alarm information to ensure that any new anomalies or risks can be discovered and responded to in a timely manner.

[0064] The alarm information processing module processes the alarm information according to the optimized sorting, gives priority to responding to alarms that may cause major problems and continuously monitors the processing effect of the alarm information. It regularly re-evaluates the stored alarm information through fuzzy entropy technology. If new anomalies or risks are detected, the alarm information processing module will dynamically adjust the alarm processing strategy and trigger the necessary emergency response.

[0065] For example: Suppose that in the past alarm records of a transformer, when the ambient temperature and load simultaneously exceed a certain critical value, it often causes the equipment to overload and fail. The multi-head neural attention model will automatically recognize this feature and increase the weight of the current alarm information. The system then dynamically adjusts the alarm sorting according to the optimized weight to ensure that the critical alarm is handled first. In addition, the system will periodically re-evaluate the stored alarm information. If new environmental changes or equipment anomalies are detected, the system will dynamically adjust the alarm processing strategy and may trigger new emergency response measures. In this way, the system can maintain efficient alarm management under different operating conditions to ensure the stable operation of the smart grid.

[0066] Specifically, through fuzzy entropy calculation, multi-head neural attention model and dynamic alarm processing, the system realizes intelligent management and response optimization of power equipment alarm information. This design can not only improve the safety and reliability of the system, but also effectively prevent key alarms from being ignored or delayed, ensuring the stable operation of the power system and distributed energy system.

[0067] The present invention is not limited to the above-mentioned embodiments. Anyone should be aware that any structural changes made under the enlightenment of the present invention, and any technical solutions that are the same or similar to the present invention, fall within the protection scope of the present invention. The technology, shape, and structural parts not described in detail in the present invention are all well-known technologies.

Claims

1. A blockchain-based power information security storage and supervision system, characterized in that: include: Data collection module, delay-tolerant network (DTN) module, data slicing and encryption module, blockchain network module, zero-knowledge proof (ZKP) module, alarm information processing module, storage node management unit; A data acquisition module, used for collecting power data from the power equipment in real time and transmitting the data to the delay tolerant network module; A delay tolerant network module, used for prioritizing the power data according to the time sensitivity of the data, and transmitting the classified data to the data slicing and encryption module; A data slicing and encryption module is used to slice the priority-classified power data into multiple data blocks, and encrypt each data block to generate an encrypted data block; A blockchain network module, which is used to receive encrypted data blocks and distribute the encrypted data blocks to storage nodes of multiple substations for distributed storage through blockchain technology. The blockchain network implements distributed storage and management of data and records the storage path of data blocks based on Hyperledger Fabric; A zero-knowledge proof module is used to verify the validity of the encrypted data block based on the zero-knowledge proof protocol in the blockchain network to ensure the confidentiality and integrity of the data in distributed storage; The alarm information processing module is used to collect and store alarm information related to power data. By analyzing historical alarm data, environmental parameters, equipment status and other information, it uses the multi-head neural attention mechanism and fuzzy entropy technology to predict possible risks in the future. Finally, it dynamically adjusts the weight of the alarm information, identifies those alarms that may cause major problems in the future, and prioritizes the allocation of processing resources for this information. The storage node management unit is used to manage and maintain the storage nodes distributed in multiple substations to ensure the security and traceability of the distributed storage of encrypted data blocks.

2. According to the blockchain-based power information security storage and supervision system of claim 1, it is characterized in that: When the data is prioritized according to the time sensitivity of the power data, a time sensitivity analysis is performed on the collected power data to obtain the sensitivity level of the power data, and the power data is classified according to preset priority rules based on the time sensitivity analysis, and the classified data is transmitted to the data slicing and encryption modules respectively according to the priority of the data.

3. According to the blockchain-based power information security storage and supervision system of claim 2, it is characterized in that: The sensitivity levels include high sensitivity, medium sensitivity and low sensitivity, among which high sensitivity data is given the highest priority, medium sensitivity and low sensitivity data correspond to medium priority and low priority respectively.

4. According to the blockchain-based power information security storage and supervision system of claim 2, it is characterized in that: Data slicing requires a preset slicing strategy before slicing. After obtaining the power information data, the received power data is sliced ​​into multiple independent data blocks according to the preset slicing strategy. After each execution of the data slicing strategy, the system records the details of the slicing strategy and the execution results on the blockchain; After obtaining independent data blocks, an asymmetric encryption algorithm is used to encrypt each data block after segmentation, and all data blocks are converted into encrypted data blocks. Each encrypted data block is accompanied by a unique encryption identifier. Finally, the generated encrypted data blocks will be transmitted to each storage node in the blockchain network for distributed storage.

5. According to the blockchain-based power information security storage and supervision system of claim 1, it is characterized in that: Before uploading data to the blockchain network, each substation storage node needs to be configured with a Hyperledger Fabric node. When the blockchain network module receives an encrypted data block containing metadata, the system uses a node selection algorithm to select a suitable node cluster from the available substation storage nodes based on the current node load, network bandwidth, storage capacity, and geographic location. The blockchain network generates a data block distribution instruction, instructing the distribution of encrypted data blocks to the selected storage nodes. After the distribution is completed, the blockchain network records the storage path, storage node identifier and related metadata of each encrypted data block in the distributed ledger of the blockchain.

6. According to the blockchain-based power information security storage and supervision system of claim 1, it is characterized in that: During the distribution process, the blockchain network will dynamically select substation storage nodes suitable for storing these data blocks based on the metadata of the encrypted data blocks and the current state of the blockchain network, and allocate high-priority data blocks to nodes with better performance. The system will also regularly verify the paths of the stored data blocks through the blockchain network and check their storage status at each node. If an abnormality is detected, the blockchain network will automatically reallocate the storage node.

7. According to the blockchain-based power information security storage and supervision system of claim 1, it is characterized in that: After the encrypted data block is generated, the node that owns the data block calculates a hierarchical zero-knowledge proof through the generator. Different levels of proof complexity are selected according to the sensitivity and purpose of the data block. For highly sensitive data blocks, a multi-level proof mechanism is adopted, each layer provides additional security verification, and uses a quantum-resistant encryption algorithm to generate a label commitment σ i , verification factor τ i , whose expression is: Among them, ε lattice is a lattice-based encryption function used to generate quantum-secure label commitments, M i is the data message or its summary at level i, r i is a random number used to ensure the randomness and security of the encryption process, σ i The tag commitment generated for level i is the encrypted message M i and a random number r i The hash value of is a verification function based on lattice theory, used to generate quantum-safe verification factors, K i is the shared key or verification parameter of the i-th level, τ i The verification factor generated for level i is the hash of the tag commitment and the verification parameter, is a hash function used to generate a secure hash value; After obtaining the hierarchical zero-knowledge proof, it is distributed to each storage node in the blockchain network together with the corresponding encrypted data block. After receiving the encrypted data block and its hierarchical zero-knowledge proof, the storage node starts the verification process. The validator node uses the shared verification parameters to independently verify each level of proof according to the hierarchical zero-knowledge proof protocol. The expression is: Among them, V layered It is the verification result of the layered zero-knowledge proof. If all layers are verified, the value is true. is the verification function of the i-th level, responsible for verifying whether the label commitment and verification factor of this level are correct, σ i , τ i is the label commitment and verification factor of the i-th level, K i is the shared key or authentication parameter of the i-th level; If all levels of verification pass, the data block will be officially stored in the distributed storage system, and the verification results and related information will be recorded in the distributed ledger of the blockchain. If the verification fails, the storage node will refuse to store the data block and will not add it to the distributed storage of the system. It will immediately issue an alarm, notify and send the alarm information to the alarm information processing module.

8. According to the blockchain-based power information security storage and supervision system of claim 1, it is characterized in that: The alarm information processing module performs the following steps during priority processing: The alarm information processing module collects the alarm information of power equipment in real time, pre-processes the data, extracts time series characteristics, environmental parameters and historical alarm record information, and performs fuzzy entropy on the time series data of the alarm information. Calculate and obtain the complexity and uncertainty index of each alarm information, fuzzy entropy The expression to be calculated is: in, represents a time series segment of length m, r is the fuzziness of the sequence, n represents the number of data points in the sequence, r represents the allowed fuzziness threshold, which is used to control the similarity between two sequence segments, N is the total number of data points, is the fuzziness function, and the sequence fragment X is calculated i and similarities between; Use historical data to build and train a multi-head neural attention model, input the complexity and uncertainty indicators calculated by fuzzy entropy and other alarm features into the multi-head neural attention model. Different attention heads of the multi-head neural attention model focus on different features, and independently learn the importance and relevance of these features based on the output results of fuzzy entropy. According to the results calculated by fuzzy entropy and multi-head neural attention mechanism, the weight of the alarm information is dynamically adjusted to optimize the alarm sorting. The expression is: W alert is the dynamic weight of the warning information, α i is the weight coefficient of the i-th attention head, calculated by the multi-head attention mechanism, β is the weight coefficient of the fuzzy entropy, which is used to balance the influence of the fuzzy entropy in the final weight; The alarm information processing module processes the alarm information according to the optimized sorting, gives priority to responding to alarms that may cause major problems and continuously monitors the processing effect of the alarm information. It regularly re-evaluates the stored alarm information through fuzzy entropy technology. If new anomalies or risks are detected, the alarm information processing module will dynamically adjust the alarm processing strategy and trigger the necessary emergency response.

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