Photovoltaic power station data security management system based on block chain and multi-dimensional technology fusion

Abstract

The invention discloses a photovoltaic power station data security management system based on block chain and multi-dimensional technology fusion, and relates to the technical field of photovoltaic data management, the photovoltaic power station data security management system comprises a data management system, the data management system comprises an edge computing node, a block chain network, an AI decision module, a privacy computing module and a cross-chain intercommunication module, the method has the advantages that invalid data removal and valid data compression are performed on original data through an anomaly detection and data compression unit of the edge computing node, and a lightweight data packet with a timestamp and an equipment identifier and a unique hash value are generated; and the block chain alliance chain adopts a PBFT consensus mechanism and distributed storage, data packet hash is stored according to a time sequence, and cooperation of local data preprocessing and credible storage is realized.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic data management technology, specifically a photovoltaic power plant data security management system based on the integration of blockchain and multidimensional technology. Background Technology

[0002] Traditional photovoltaic power plants face systemic defects in the data collection and preprocessing stages during data management. Raw data often contains a large amount of invalid information and lacks effective real-time filtering and optimization mechanisms, resulting in high data redundancy and heavy storage pressure. At the same time, the centralized data storage architecture is susceptible to single point of failure or malicious attacks, making it difficult to guarantee data integrity and traceability, which in turn affects the accuracy of subsequent data analysis and the reliability of decision-making.

[0003] At the level of multi-entity collaboration and data value mining, the existing technology system has significant bottlenecks. There is a lack of standardized and secure collaboration mechanisms for data interoperability between different power plants, operation and maintenance teams and external regulatory systems. It is difficult to balance the needs of privacy protection and information verification during data sharing, resulting in low efficiency of cross-entity data collaboration. In addition, with the evolution of the technological environment, traditional data security protection methods are insufficient to resist emerging risks and cannot meet the dual needs of long-term data security and value release, which restricts the data-driven intelligent upgrade of the photovoltaic industry. To this end, we propose a photovoltaic power plant data security management system based on the integration of blockchain and multi-dimensional technologies. Summary of the Invention

[0004] The purpose of this invention is to provide a data security management system for photovoltaic power plants based on the integration of blockchain and multidimensional technologies.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a photovoltaic power station data security management system based on the integration of blockchain and multi-dimensional technology, comprising a data management system, wherein the data management system includes an edge computing node, a blockchain network, an AI decision-making module, a privacy computing module, and a cross-chain interoperability module;

[0006] The edge computing node is deployed on the photovoltaic power station body to connect to the sensor for data acquisition. The edge computing node is equipped with an anomaly detection and data compression unit. The anomaly detection and data compression unit is used to preprocess the raw data and output lightweight data packets. A unique hash value is generated for the lightweight data packets through a hash algorithm.

[0007] The blockchain network adopts a consortium blockchain. By setting up the PBFT consensus mechanism and a smart contract engine, the PBFT consensus mechanism is used to achieve consensus on the transaction data of hash values, and the smart contract engine automatically responds to the contract.

[0008] The AI ​​decision-making module is equipped with an LSTM hybrid model, which is trained by accessing trusted data in the blockchain network. After training, the diagnostic results output by the LSTM hybrid model are transmitted to the smart contract.

[0009] The privacy computing module is integrated into the blockchain network, and the privacy computing module uses a zero-knowledge proof protocol to generate data attribute verification proofs;

[0010] The cross-chain interoperability module is configured with a cross-chain gateway, which is used to relay data from the consortium blockchain (relay methods include but are not limited to point-to-point transmission, network protocol transmission, API interface call, message queue relay, and edge-cloud relay) to external systems.

[0011] As a further aspect of the present invention: the anomaly detection and data compression unit distinguishes between invalid and valid data in power saving, then compresses the valid data and generates a lightweight data packet with a timestamp and device identifier. The edge computing node is also equipped with a local storage unit, which is used to temporarily store the original data and the lightweight data packet. The local storage unit periodically cleans up the original data and the lightweight data packet.

[0012] As a further aspect of the present invention: the consortium blockchain architecture of the blockchain network includes multiple consensus nodes, adopts a Byzantine fault-tolerant consensus mechanism, the distributed storage unit stores data packet hashes in chronological order, and the smart contract engine deploys an automatic response contract linked to AI diagnostic results. The smart contract includes fault work order generation and device permission management logic.

[0013] As a further aspect of the present invention: the AI ​​decision module uses an adaptive learning rate algorithm to update the parameters of the LSTM hybrid model, the specific algorithm of which is as follows:

[0014] ;

[0015] in, In order to be in Learning rate at any given moment The initial learning rate, The attenuation coefficient is... In order to be in Momentary data distribution drift The LSTM hybrid model uses a natural exponential function and an adaptive learning rate algorithm to improve stability when the data distribution changes.

[0016] As a further aspect of the present invention: the data distribution drift is generated by an adversarial network to determine the distribution difference of the data distribution drift, and the calculation formula for the data distribution drift is as follows:

[0017] ;

[0018] in, for Time-based statistical feature drift, for Constantly combat the distributional variability of the network output. , These are the weighting coefficients, and The adversarial network enhances the accuracy of data drift detection.

[0019] As a further aspect of the present invention: the zero-knowledge proof protocol of the privacy computing module includes a proof generation unit and a verification interface. The proof generation unit generates a verification proof containing data attribute features based on the original data. The verification interface is provided for external verification parties to verify the validity of the proof. The proof generation process does not disclose the original data content. The verification proof is associated with the blockchain evidence data through hash anchoring.

[0020] As a further aspect of the present invention: the cross-chain gateway of the cross-chain interoperability module is developed based on the blockchain cross-chain framework, supports protocol compatibility with various external business system blockchains, the event listening unit captures on-chain smart contract events in real time, and the data relay unit sends event data and corresponding hash values ​​to external systems through an encrypted channel to achieve cross-chain data consistency anchoring.

[0021] As a further aspect of the present invention, the data management system further includes a quantum-resistant encryption module, which employs an encryption algorithm based on the post-quantum cryptography standard. The security strength of the encryption algorithm is measured by evaluating its ability to resist known quantum attack algorithms, thereby ensuring the security redundancy of the encryption system in a quantum computing environment.

[0022] Compared with the prior art, the beneficial effects of the present invention by adopting the above technical solution are as follows:

[0023] 1. This invention uses the anomaly detection and data compression unit of the edge computing node to remove invalid data and compress valid data from the original data, generating a lightweight data packet with timestamp and device identifier and a unique hash value, reducing storage pressure. The blockchain consortium chain adopts the PBFT consensus mechanism and distributed storage, and stores the hash of the data packet in time order, solving the problems of data redundancy, high storage cost and insufficient integrity and traceability under the traditional centralized architecture, and realizing the synergy of local data preprocessing and trusted storage.

[0024] 2. This invention trains an LSTM hybrid model based on trusted blockchain data through an AI decision-making module, dynamically adjusts parameters through an adaptive learning rate algorithm, and combines adversarial networks to enhance data distribution drift detection, ensuring model stability when data distribution changes. The diagnostic results are linked to smart contracts to automatically generate fault work orders, solving the problems of low efficiency and delayed fault response in traditional manual inspection.

[0025] 3. This invention generates data attribute verification proofs through a privacy computing module using zero-knowledge proofs, ensuring that original information is not leaked during data sharing. The cross-chain gateway relays data and anchors consistency based on the blockchain cross-chain framework. The quantum-resistant encryption module uses a post-quantum algorithm to evaluate security strength, solving the problems of conflict between privacy protection and verification requirements in cross-entity collaboration, cross-chain data consistency, and insufficient redundancy of traditional encryption security under the threat of quantum computing. It supports secure interoperability of multiple systems and long-term data protection. Attached Figure Description

[0026] Figure 1 This is a diagram illustrating the overall system architecture and data flow in an embodiment of the present invention.

[0027] Figure 2 This is a flowchart of edge computing node data processing in an embodiment of the present invention;

[0028] Figure 3 This is a flowchart illustrating the AI ​​diagnosis and smart contract response process in an embodiment of the present invention.

[0029] Figure 4 This is a flowchart of the quantum security strength assessment in an embodiment of the present invention. Detailed Implementation

[0030] The specific embodiments of the present invention will be further described below with reference to the accompanying drawings. It should be noted that the description of these embodiments is for the purpose of helping to understand the present invention, but does not constitute a limitation of the present invention.

[0031] Furthermore, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0032] Please see the appendix Figure 1 -Appendix Figure 4 This invention relates to a photovoltaic power plant data security management system based on the integration of blockchain and multi-dimensional technology. The photovoltaic power plant data security management system based on the integration of blockchain and multi-dimensional technology includes a data management system, which includes edge computing nodes, a blockchain network, an AI decision-making module, a privacy computing module, and a cross-chain interoperability module.

[0033] Edge computing nodes are deployed on the photovoltaic power station itself to connect to the sensor for data acquisition. The edge computing nodes are equipped with an anomaly detection and data compression unit. The anomaly detection and data compression unit is used to preprocess the raw data and output lightweight data packets. A unique hash value is generated for the lightweight data packets through a hash algorithm.

[0034] The blockchain network adopts a consortium blockchain. By setting up the PBFT consensus mechanism and a smart contract engine, the PBFT consensus mechanism is used to achieve consensus on the transaction data of hash values, and the smart contract engine automatically responds to the contract.

[0035] The AI ​​decision-making module is equipped with an LSTM hybrid model. The LSTM hybrid model is trained by accessing trusted data in the blockchain network. After training is completed, the diagnostic results output by the LSTM hybrid model are transmitted to the smart contract.

[0036] The privacy computing module is integrated into the blockchain network, and it uses a zero-knowledge proof protocol to generate data attribute verification proofs.

[0037] The cross-chain interoperability module configures a cross-chain gateway, which is used to securely relay data from the consortium blockchain to external systems.

[0038] In one embodiment of the present invention: the anomaly detection and data compression unit distinguishes between invalid and valid data in power saving, then compresses the valid data and generates a lightweight data packet with timestamp and device identifier. The edge computing node is also equipped with a local storage unit, which is used to temporarily store the original data and the lightweight data packet. The local storage unit cleans up the original data and the lightweight data packet periodically.

[0039] In one embodiment of the present invention: the consortium blockchain architecture of the blockchain network includes multiple consensus nodes, adopts the Byzantine fault-tolerant consensus mechanism, the distributed storage unit stores data packet hashes in chronological order, the smart contract engine deploys an automatic response contract linked to AI diagnostic results, and the smart contract includes fault work order generation and device permission management logic.

[0040] In one embodiment of the present invention: the AI ​​decision module uses an adaptive learning rate algorithm to update the parameters of the LSTM hybrid model, the specific algorithm being as follows:

[0041] ;

[0042] in, In order to be in Learning rate at any given moment The initial learning rate, The attenuation coefficient is... In order to be in Momentary data distribution drift Using the natural exponential function, the LSTM hybrid model improves stability when the data distribution changes through an adaptive learning rate algorithm.

[0043] In one embodiment of the present invention: the data distribution drift is generated by an adversarial network to determine the distribution difference of the data distribution drift. The calculation formula for the data distribution drift is as follows:

[0044] ;

[0045] in, for Time-based statistical feature drift, for Constantly combat the distributional variability of the network output. , These are the weighting coefficients, and The accuracy of data drift detection is enhanced by using adversarial networks.

[0046] In one embodiment of the present invention: the zero-knowledge proof protocol of the privacy computing module includes a proof generation unit and a verification interface. The proof generation unit generates a verification proof containing data attribute features based on the original data. The verification interface is provided for external verification parties to verify the validity of the proof. The proof generation process does not disclose the original data content. The verification proof is associated with the blockchain evidence data through hash anchoring.

[0047] In one embodiment of the present invention: the cross-chain gateway of the cross-chain interoperability module is developed based on the blockchain cross-chain framework, supports protocol compatibility with various external business system blockchains, the event listening unit captures on-chain smart contract events in real time, and the data relay unit sends event data and corresponding hash values ​​to external systems through an encrypted channel to achieve cross-chain data consistency anchoring.

[0048] In one embodiment of the present invention, the data management system further includes a quantum-resistant encryption module. The quantum-resistant encryption module adopts a lattice-based encryption algorithm (such as NTRU) that conforms to the NIST post-quantum cryptography standard. Its security strength is guaranteed by the algorithm's publicly disclosed resistance to quantum attacks, which has been evaluated by the cryptographic community. For example, the NTRU-761 algorithm, which has a security strength equivalent to 256-bit conventional encryption, is adopted to cope with the threat of future quantum computing.

[0049] Example 1: Local Data Processing and Blockchain Storage for a Single Photovoltaic Power Station

[0050] Application scenarios

[0051] This embodiment addresses the local data security management needs of a 10MW centralized photovoltaic power station, focusing on the collaborative workflow between edge computing nodes and blockchain networks to achieve real-time preprocessing, lightweight transmission, and reliable storage of sensor data. The power station is equipped with 2,000 photovoltaic panels (equipped with temperature sensors and light intensity sensors), 50 inverters (output power sensors), and 10 combiner boxes (current / voltage sensors), generating approximately 800GB of raw monitoring data daily. Issues such as data redundancy, storage pressure, and traceability reliability need to be addressed.

[0052] Technical details

[0053] 1. Data preprocessing for edge computing nodes

[0054] Edge computing nodes are deployed on a local server in the power plant control room. They connect to various sensors via an industrial bus (Modbus TCP protocol) to collect data in real time, such as photovoltaic panel temperature (sampling frequency 1Hz), illuminance (0.5Hz), and inverter output power (2Hz). The anomaly detection and data compression unit adopts a "threshold filtering + wavelet transform compression" strategy.

[0055] Invalid data identification: Sensor malfunction is determined by setting a preset threshold (e.g., temperature > 85℃, light intensity < 50W / m). 2 (Values ​​deemed invalid at that time) are removed, eliminating approximately 5% of invalid data for that day;

[0056] Effective data compression: Wavelet transform (db4 wavelet basis, decomposition level 3) was used to compress the remaining 95% of effective data, reducing the daily temperature data of a single photovoltaic panel from 1440 sampling points to 288 feature points, with a compression ratio of 5:1.

[0057] Lightweight data packet generation: Adds a timestamp (accurate to milliseconds) and device identifier to the compressed data, and generates a unique hash value using the SHA-256 hash algorithm.

[0058] The local storage unit (1TB SSD) temporarily stores raw data and lightweight data packets. A cleanup task is performed at 2:00 AM every day, retaining only the data for the past 72 hours to free up storage space.

[0059] 2. Consortium blockchain evidence storage in blockchain networks

[0060] The initial configuration of the blockchain network consists of 5 consensus nodes (one each from the power plant's local server, operation and maintenance center, equipment manufacturer, third-party testing agency, and power grid dispatch center), employing a consortium blockchain architecture and the PBFT consensus mechanism (consensus latency < 500ms).

[0061] Distributed storage: Distributed storage units store lightweight data packet hashes in chronological order, forming an immutable evidence chain;

[0062] Basic smart contract functions: Deploy a "data storage record contract" to automatically record the device identifier, timestamp and data type corresponding to each hash value, and support querying historical storage data by device ID or time range;

[0063] At 15:00 that day, the maintenance personnel retrieved the temperature data hash of photovoltaic panel No. 3 through the contract query interface, compared it with the original data hash stored locally, and verified that the data had not been tampered with, thus completing the first local data traceability.

[0064] Example 2: Multi-Power Plant Consortium Chain Collaboration and AI Fault Diagnosis

[0065] Application scenarios

[0066] Based on Example 1, the system is extended to three adjacent photovoltaic power plants (Power Plants A, B, and C, with a total installed capacity of 30MW) to form a regional alliance chain. Focusing on the fault diagnosis function of the AI ​​decision-making module, the system trains an LSTM hybrid model with trusted data from multiple power plants to achieve real-time diagnosis of inverter faults and intelligent work order generation, thus solving the problems of low efficiency and delayed fault response in traditional manual inspections.

[0067] Technical details

[0068] 1. Consortium blockchain collaboration and data sharing

[0069] The blockchain network has been expanded to 15 consensus nodes (3 nodes per power station, and 6 nodes for regional operation and maintenance centers, power grid dispatching, etc.), and adopts an improved PBFT consensus mechanism (fault-tolerant nodes ≤ 4, consensus throughput increased to 200 TPS):

[0070] Cross-power station data storage: The edge computing nodes of power stations A, B, and C all upload lightweight data packets to the consortium blockchain through a cross-chain gateway (initially only internally). The distributed storage unit stores data in partitions according to the power station ID, supporting cross-power station data correlation queries (such as comparing the correlation between solar irradiance and power generation of power stations A and B on the same day).

[0071] Smart contract upgrade: Deploy "AI diagnostic linkage contract" to integrate fault work order generation logic (including fault type, device location, priority fields) and device permission management (only maintenance personnel can trigger work order processing);

[0072] 2. Fault diagnosis of the AI ​​decision-making module

[0073] The AI ​​decision-making module is deployed on the regional operations and maintenance center server. The LSTM hybrid model (which combines LSTM and CNN, with LSTM used for time series features and CNN used for extracting spatial features of the photovoltaic panel array) accesses three months of trusted data in the consortium blockchain (the original data corresponding to a total of 120 million hashes is authorized, decrypted, and then used for training).

[0074] Model Training and Adaptive Learning Rate: Input features include inverter output power (time series) and photovoltaic panel temperature distribution (spatial features). An adaptive learning rate algorithm is used to update parameters.

[0075] ;

[0076] Among them, the initial learning rate =1, attenuation coefficient =0.05, data distribution drift Through formula calculate( =0.6, =0.4; The statistical characteristics of the differences in sunshine data between September and June are shown. To combat the distribution variability of network output, when seasonal changes cause the distribution of illumination data to drift ( When the learning rate is 0.3, To avoid model overfitting;

[0077] Real-time diagnosis and work order triggering: After the model training is completed (accuracy reaches 92%), the power data hash of inverter "INV-12" of power station A in the consortium blockchain is received in real time. After parsing, it is input into the model and outputs the diagnosis result "capacitor aging causes power fluctuation (confidence level 96%)" and transmitted to the smart contract. The contract automatically generates a work order (priority "high", location "Inverter No. 3 in the North Zone of Power Station A") and pushes it to the mobile terminal of the operation and maintenance personnel. The response delay is less than 3 minutes.

[0078] Example 3: Cross-chain data sharing and quantum security protection

[0079] Application scenarios

[0080] Based on Example 2, a cross-chain interoperability module and a quantum-resistant encryption module are introduced to achieve secure data sharing between the regional consortium blockchain and external energy regulatory platforms and power grid dispatching systems, and to resist potential threats from quantum computing, thereby supporting the needs of new energy power trading and regulatory compliance.

[0081] Technical details

[0082] 1. Cross-chain data sharing and privacy computation

[0083] The cross-chain interoperability module configures a two-way cross-chain gateway (developed based on the Polkadot cross-chain framework), supporting protocol compatibility with energy regulatory chains (HyperledgerFabric architecture) and power grid dispatch chains (Ethereum consortium chains).

[0084] Zero-knowledge proof data verification: When the regulatory platform needs to verify the "grid-connected power compliance" of power plant A, the privacy computing module generates a verification certificate based on the zero-knowledge proof protocol (zk-SNARKs): The certificate generation unit extracts the attribute features of power plant A's grid power data (such as "total power ≥ 1 million kWh" and "peak-valley power ratio meets the standard"), and generates a certificate document that does not require the disclosure of specific power values; the regulatory platform verifies the validity of the certificate through the verification interface (verification time < 2 seconds) and confirms the data compliance;

[0085] Cross-chain relay and consistency anchoring: The event listening unit of the cross-chain gateway captures the "monthly electricity statistics" smart contract event in the consortium chain in real time. The data relay unit relays the event data (including electricity hash and timestamp) to the regulatory chain through the TLS1.3 encrypted channel. At the same time, the cross-chain transaction ID is recorded in the consortium chain and the regulatory chain respectively to achieve data consistency anchoring.

[0086] 2. Quantum-resistant encryption protection

[0087] The quantum-resistant encryption module is integrated into the blockchain network and cross-chain gateway. To address the long-term security challenges brought about by quantum computing, the module adopts a post-quantum cryptography algorithm that has been widely evaluated by the international cryptography community. Specifically, it selects a lattice-based encryption algorithm (such as NTRU) that conforms to the post-quantum cryptography standardization process of the National Institute of Standards and Technology (NIST).

[0088] In terms of algorithm selection, the module is based on the traditional RSA-2048 algorithm (whose security strength is equivalent to about 112 bits) and upgraded to a post-quantum algorithm with higher recognized security strength. For example, the NTRU-761 algorithm is adopted, which is designed with a security strength equivalent to 256 bits of traditional encryption and can effectively resist known quantum attack algorithms (such as Shor's algorithm), thus providing long-term security redundancy for cross-chain data transmission and blockchain evidence storage.

[0089] The power grid dispatching system calls the real-time output data of power plant B in the consortium blockchain (transmitted via quantum encryption) through a cross-chain gateway, and combines it with the AI ​​decision-making module to predict short-term power generation and optimize the regional power dispatching plan.

[0090] While the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the invention. Any variations and modifications can be made by those skilled in the art without departing from the spirit and scope of the invention. Therefore, any modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention, without departing from the scope of the invention, fall within the protection scope defined by the claims of the present invention.

Claims

1. A photovoltaic power plant data security management system based on the integration of blockchain and multi-dimensional technology, comprising a data management system, characterized in that: The data management system includes edge computing nodes, a blockchain network, an AI decision-making module, a privacy computing module, and a cross-chain interoperability module; The edge computing node is deployed on the photovoltaic power station body to connect to the sensor for data acquisition. The edge computing node is equipped with an anomaly detection and data compression unit. The anomaly detection and data compression unit is used to preprocess the raw data and output lightweight data packets. A unique hash value is generated for the lightweight data packets through a hash algorithm. The blockchain network adopts a consortium blockchain. By setting up the PBFT consensus mechanism and a smart contract engine, the PBFT consensus mechanism is used to achieve consensus on the transaction data of hash values, and the smart contract engine automatically responds to the contract. The AI ​​decision-making module is equipped with an LSTM hybrid model, which is trained by accessing trusted data in the blockchain network. After training, the diagnostic results output by the LSTM hybrid model are transmitted to the smart contract. The privacy computing module is integrated into the blockchain network, and the privacy computing module uses a zero-knowledge proof protocol to generate data attribute verification proofs; The cross-chain interoperability module is configured with a cross-chain gateway, which is used to relay data from the consortium blockchain to external systems.

2. The photovoltaic power station data security management system based on the integration of blockchain and multi-dimensional technology as described in claim 1, characterized in that: The anomaly detection and data compression unit distinguishes between invalid and valid data during power saving, then compresses the valid data and generates a lightweight data packet with a timestamp and device identifier. The edge computing node is also equipped with a local storage unit, which is used to temporarily store the original data and the lightweight data packet. The local storage unit cleans up the original data and the lightweight data packet periodically.

3. The photovoltaic power plant data security management system based on the integration of blockchain and multi-dimensional technology as described in claim 2, characterized in that: The consortium blockchain architecture of the blockchain network includes multiple consensus nodes, adopts a Byzantine fault-tolerant consensus mechanism, and the distributed storage unit stores data packet hashes in chronological order. The smart contract engine is deployed with an automatic response contract linked to AI diagnostic results. The smart contract includes logic for generating fault work orders and managing device permissions.

4. The photovoltaic power plant data security management system based on the integration of blockchain and multi-dimensional technology as described in claim 3, characterized in that: The AI ​​decision-making module uses an adaptive learning rate algorithm to update the parameters of the LSTM hybrid model. The specific algorithm is as follows: ； in, In order to be in Learning rate at any given moment The initial learning rate, The attenuation coefficient is... In order to be in Momentary data distribution drift The LSTM hybrid model uses a natural exponential function and an adaptive learning rate algorithm to improve stability when the data distribution changes.

5. The photovoltaic power plant data security management system based on the integration of blockchain and multi-dimensional technology as described in claim 4, characterized in that: The data distribution drift is generated using an adversarial network to determine the distribution difference of the data distribution drift. The formula for calculating the data distribution drift is as follows: ； in, for Time-based statistical feature drift, for Constantly combat the distributional variability of the network output. , These are the weighting coefficients, and The adversarial network enhances the accuracy of data drift detection.

6. The photovoltaic power plant data security management system based on the integration of blockchain and multi-dimensional technology as described in claim 5, characterized in that: The zero-knowledge proof protocol of the privacy computing module includes a proof generation unit and a verification interface. The proof generation unit generates a verification proof containing data attribute features based on the original data. The verification interface allows external verifiers to verify the validity of the proof. The proof generation process does not disclose the original data content. The verification proof is associated with the blockchain-stored evidence data through hash anchoring.

7. The photovoltaic power plant data security management system based on the integration of blockchain and multi-dimensional technology as described in claim 6, characterized in that: The cross-chain interoperability module's cross-chain gateway is developed based on a blockchain cross-chain framework, supporting protocol compatibility with various external business system blockchains. The event listening unit captures on-chain smart contract events in real time, and the data relay unit sends event data and corresponding hash values ​​to external systems through an encrypted channel, achieving cross-chain data consistency anchoring.

8. The photovoltaic power plant data security management system based on the integration of blockchain and multi-dimensional technology according to claim 7, characterized in that: The data management system also includes a quantum-resistant encryption module, which employs an encryption algorithm based on the post-quantum cryptography standard. The security strength of the encryption algorithm is measured by evaluating its ability to resist known quantum attack algorithms, so as to ensure the security redundancy of the encryption system in a quantum computing environment.

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