Energy monitoring data storage method based on block chain

Abstract

The invention discloses an energy monitoring data storage method based on a block chain. The method comprises the steps of energy monitoring data collection, data storage structure construction, data access control, data query verification and data monitoring report generation. The invention belongs to the technical field of data processing, and particularly relates to an energy monitoring data storage method based on a block chain, which adopts the design of constructing user data block batch processing and weighting Merkle tree to generate dynamic weight fingerprints, greatly reduces the uplink frequency, adopts the simplified information such as storing original data under the chain and storing fingerprints on the chain, and improves the data storage efficiency. The block chain transaction processing pressure is relieved; dynamic risk scoring and gradient access control are realized by deploying an intelligent contract, data access authority is limited in combination with data sensitivity and requester credibility, only a trusted subject is authorized to acquire data of a corresponding level, the credibility is updated in real time to form a risk control closed loop, energy monitoring data is protected from the source, and privacy leakage is avoided.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of data processing, and is specifically an energy monitoring data storage method based on a blockchain. BACKGROUND

[0002] The energy monitoring data storage method is a new type of data storage and management method that utilizes the characteristics of blockchain technology to solve the problems of data credibility, security and sharing in the energy internet. However, the existing energy monitoring data storage method has the technical problems that the energy monitoring data is massive and high-frequency, and the transaction processing capacity of most blockchains is limited, the data chaining delay is high, the transaction confirmation is slow, and it is difficult to meet the real-time monitoring requirements; the energy monitoring data is highly sensitive private information that can reflect the user's living habits, whether at home or other information, and data protection is not in place, which can easily cause serious privacy leakage problems for users. SUMMARY

[0003] In view of the above situation, in order to overcome the defects of the prior art, the application provides an energy monitoring data storage method based on a blockchain, which solves the technical problems that the energy monitoring data is massive and high-frequency, and the transaction processing capacity of most blockchains is limited, the data chaining delay is high, the transaction confirmation is slow, and it is difficult to meet the real-time monitoring requirements by adopting the design of constructing user data block batch processing and generating dynamic weight fingerprints of weighted Merkle tree, which compresses the chaining operation of N data packets into one time, greatly reduces the chaining frequency, stores the original data off-chain and stores the fingerprint and other simplified information on-chain, reduces the amount of data on-chain, relieves the transaction processing pressure of the blockchain, and adapts to the monitoring requirements of massive and high-frequency energy data; solves the technical problems that the energy monitoring data is highly sensitive private information that can reflect the user's living habits, whether at home or other information, and data protection is not in place, which can easily cause serious privacy leakage problems for users by deploying a smart contract to realize dynamic risk scoring and gradient access control, limiting data access permissions in combination with data sensitivity and requester credibility, only authorizing trusted subjects to obtain corresponding level data, and updating the credibility in real time to form a risk control closed loop, thereby protecting the energy monitoring data from the source and avoiding privacy leakage.

[0004] The technical scheme adopted by the application is as follows: the energy monitoring data storage method based on a blockchain provided by the application comprises the following steps:

[0005] Step S1: energy monitoring data acquisition;

[0006] Step S2: constructing a data storage structure;

[0007] Step S3: data access control;

[0008] Step S4: data query verification;

[0009] Step S5: generating a data monitoring report.

[0010] Further, in step S1, the energy monitoring data collection, specifically, real-time collection of raw data of energy monitoring data from distributed energy nodes; the edge gateway performs preliminary processing on the data, data cleaning and data aggregation, and generates data fingerprints.

[0011] Further, in step S2, the data storage structure is constructed, including the following steps:

[0012] Step S21: off-chain storage, storing the preliminary processed energy monitoring data in the off-chain distributed storage system, and generating a unique content identifier;

[0013] Step S22: constructing an on-chain data unit, constructing a user data block containing data packets, calculating the dynamic weight of each data packet, constructing a weighted Merkle tree based on HMAC and hierarchical weight to generate a dynamic weight fingerprint, forming a structured on-chain data unit containing a dynamic weight fingerprint, a content identifier, a timestamp, a device ID and metadata, realizing efficient on-chain of batch data, the dynamic weight fingerprint is the overall identifier of the batch data packet under the business weight, including the following content;

[0014] Constructing a user data block, let the user data block contain N data packets, ; Wherein N represents the total number of data packets, represents the user data block;

[0015] For each data packet , a nonlinear adjustment is introduced to calculate the dynamic weight;

[0016] Constructing a weighted Merkle tree, introducing structured binding based on HMAC, calculating the weighted leaf node hash, and inducing the hash values of all leaf nodes into a unique root hash representing the dynamic weight fingerprint of the user data block as the final on-chain data fingerprint, compressing N times of on-chain operation into 1 time, greatly saving blockchain space;

[0017] Introducing hierarchical weight to enhance the tamper resistance and business binding depth of the Merkle tree. For any parent node, its weight is the weighted average of the weights of the left and right nodes, and the hash value of the parent node is determined by the hash of its child nodes and the aggregation weight;

[0018] Step S23: data on-chain, taking the on-chain data unit as the transaction content, sending it to the blockchain network, and the nodes in the blockchain network verify and consensus the transaction through the preselected consensus mechanism. After the transaction is passed, it is packaged into a new block and permanently recorded on the distributed ledger.

[0019] Further, in step S3, the data access control includes the following steps:

[0020] Step S31: Deploy a smart contract. Deploy a smart contract to manage energy monitoring data on the blockchain network. When the data unit on the chain is written into a block, the blockchain's event mechanism is triggered. The smart contract can listen to the data on the chain by subscribing to the event.

[0021] Step S32: Introduce trend momentum. When the absolute value of trend momentum exceeds the critical threshold for multiple consecutive periods, a high-level alarm is triggered, indicating a strong and continuous deterioration trend. When the absolute value of trend momentum exceeds the critical threshold for a single time, a normal warning is triggered. Calculate the trend momentum.

[0022] Based on the trend momentum quantification data anomaly index, dynamic anomaly detection and hierarchical early warning of energy monitoring data can be achieved;

[0023] Step S33: Set up access control logic, embed access control rules in the smart contract, and introduce dynamic risk scores calculated in real time so that access control decisions can be adaptively adjusted according to the current security situation.

[0024] The smart contract executes a tiered access control strategy based on the calculated dynamic risk score: it presets a high-risk threshold and a low-risk threshold. When the dynamic risk score is less than or equal to the low-risk threshold, access to the original high-precision data is allowed; when the dynamic risk score is greater than the low-risk threshold but less than the high-risk threshold, access to the anonymized data is allowed; when the dynamic risk score is greater than the high-risk threshold, the access request is rejected and the high-risk access attempt is recorded.

[0025] Step S34: Update the requester's reputation. A reputation update function is built into the smart contract and triggered at the end of each access, forming a smart risk control system that dynamically assesses the risk of data access.

[0026] Further, in step S4, the data query verification specifically involves an authorized query. The data requester initiates a data access request to the smart contract through the DApp frontend. After verifying the permissions, the smart contract returns the corresponding dynamic weight fingerprint and content identifier. To verify the data, the requester uses the content identifier to retrieve the original data from off-chain storage, calculates the hash value of the retrieved original data, and compares the hash value with the dynamic weight fingerprint stored on-chain. If the two match, it proves that the data has not been tampered with since it was recorded and is complete and trustworthy. If they do not match, it indicates that the data has been tampered with and the trust chain has been broken.

[0027] Furthermore, in step S5, the generation of the data monitoring report specifically involves automatically generating a multi-dimensional and traceable monitoring report based on trusted energy monitoring data verified by the blockchain, combined with the abnormal warnings and access control results triggered by smart contracts, providing a basis for decision-making in energy scheduling, equipment operation and maintenance, user settlement and other businesses.

[0028] The beneficial results achieved by the present invention using the above solution are as follows:

[0029] (1) In response to the technical problems that energy monitoring data is massive and high-frequency, and most blockchains have limited transaction processing capabilities, high data uplink latency, slow transaction confirmation, and difficulty in meeting real-time monitoring needs, a design is adopted to construct user data blocks for batch processing and generate dynamic weight fingerprints using weighted Merkle trees. This compresses the uplink operation of N data packets into one operation, significantly reducing the uplink frequency. The design uses off-chain storage of raw data and on-chain storage of fingerprints and other simplified information to reduce the amount of on-chain data, alleviate the pressure of blockchain transaction processing, and adapt to the monitoring needs of massive and high-frequency energy data.

[0030] (2) In view of the technical problem that energy monitoring data is highly sensitive privacy information that can reflect users’ living habits, whether they are at home, etc., and that inadequate data protection can easily lead to serious privacy leaks, smart contracts are deployed to implement dynamic risk scoring and tiered access control. Data access permissions are restricted by combining data sensitivity and requester credibility. Only trusted entities are authorized to obtain data of the corresponding level. Credibility is updated in real time to form a risk control closed loop, protecting energy monitoring data from the source and avoiding privacy leaks. Attached Figure Description

[0031] Figure 1 A schematic diagram of the process for the blockchain-based energy monitoring data storage method provided by the present invention;

[0032] Figure 2 This is a flowchart illustrating step S2;

[0033] Figure 3 This is a flowchart illustrating step S3.

[0034] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof. Detailed Implementation

[0035] 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. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0036] In the description of this invention, it should be understood that the terms "upper", "lower", "front", "rear", "left", "right", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0037] Example 1, see Figure 1 The present invention provides a blockchain-based energy monitoring data storage method, which includes the following steps:

[0038] Step S1: Energy monitoring data acquisition, specifically, real-time acquisition of energy monitoring data from distributed energy nodes and preliminary processing;

[0039] Step S2: Construct a data storage structure, specifically by storing the pre-processed energy monitoring data in an off-chain distributed storage system, constructing user data blocks containing data packets, calculating the dynamic weight of each data packet, constructing a weighted Merkle tree based on HMAC and hierarchical weights and generating dynamic weight fingerprints, forming on-chain data units and sending them to the blockchain network as transactions;

[0040] Step S3: Data access control, specifically, deploying smart contracts on the blockchain network, calculating trend momentum to quantify the data anomaly index, combining data sensitivity and requester credibility to calculate a dynamic risk score, and performing tiered access control based on this score, updating the requester credibility after each access, forming an intelligent risk control system that assesses and responds to security risks in real time.

[0041] Step S4: Data query verification, specifically, the data requester verifies the data credibility by comparing the hash value calculated from the on-chain dynamic weight fingerprint and the off-chain original data;

[0042] Step S5: Generate a data monitoring report.

[0043] Example 2, see Figure 1This embodiment is based on the above embodiment. In step S1, the energy monitoring data acquisition specifically involves collecting raw energy monitoring data from distributed energy nodes in real time; the data is then preliminarily processed at the edge gateway, including data cleaning and aggregation, and a data fingerprint is generated.

[0044] The distributed energy nodes include smart meters, photovoltaic inverters, wind turbine sensors, and energy storage system BMS;

[0045] The energy monitoring data includes power generation, power consumption, voltage, current, frequency, power factor, equipment status, etc.

[0046] The data cleaning is used to filter out obvious outliers and noise;

[0047] The data aggregation is used to reduce the frequency of data being uploaded to the blockchain, and to aggregate short-cycle data into long-cycle statistical values.

[0048] The data fingerprint refers to the calculation of an encrypted hash value on the aggregated data packet. This hash value serves as a unique and tamper-proof data fingerprint, which is the basic integrity identifier at the data packet level.

[0049] Example 3, see Figure 1 and Figure 2 This embodiment is based on the above embodiment. In step S2, the construction of the data storage structure includes the following steps:

[0050] Step S21: Off-chain storage, storing the pre-processed energy monitoring data in an off-chain distributed storage system and generating a unique content identifier;

[0051] Step S22: Construct on-chain data units, construct user data blocks containing data packets, calculate the dynamic weight of each data packet, construct a weighted Merkle tree based on HMAC and hierarchical weights to generate dynamic weight fingerprints, and form structured on-chain data units containing dynamic weight fingerprints, content identifiers, timestamps, device IDs and metadata to achieve efficient on-chain uploading of batch data. The dynamic weight fingerprint is the overall identifier of batch data packets under the support of business weights, including the following:

[0052] Construct a user data block, assuming the user data block contains N data packets. Where N represents the total number of data packets, Represents a user data block;

[0053] For each data packet To introduce nonlinear adjustment, the dynamic weights are calculated using the following formula:

[0054] ;

[0055] In the formula, This represents the dynamic weight of the i-th data packet. This represents the absolute value of the power change for user i. This represents the standard deviation of the absolute values ​​of the power changes across all data in the current batch. Indicates the user level coefficient. This represents the information entropy of user data, used to measure the information content of the data. , , represents the weighting coefficient, tanh() represents the hyperbolic tangent function, and ln() represents the natural logarithm;

[0056] A weighted Merkle tree is constructed, and HMAC-based structured binding is introduced. The weighted leaf node hashes are calculated, and the hash values ​​of all leaf nodes are summarized into a unique root hash, representing the dynamic weight fingerprint of the user data block. This root hash serves as the final data fingerprint for on-chain processing, compressing N on-chain operations into one, greatly saving blockchain space. The formula used is as follows:

[0057] ;

[0058] ;

[0059] In the formula, This represents the hash value of the i-th leaf node. HMAC stands for Secure Hash Algorithm, a 256-bit cryptographic mechanism that uses a hash function and a key to verify message integrity and authenticity. Represents a dynamic weighted fingerprint of user data blocks, used for efficient batch processing of energy monitoring data. This represents the Merkelgen hash calculation function. Represents the hash sequence of the leaf nodes;

[0060] By introducing hierarchical weights, the Merkle tree's resistance to tampering and its business binding depth are enhanced. For any parent node, its weight is a weighted average of the weights of its left and right nodes. The hash value of the parent node is determined by the hashes of its child nodes and the aggregate weights, using the following formula:

[0061] ;

[0062] ;

[0063] In the formula, This represents the aggregation weight of the parent node. This indicates the weight of the left child node. This indicates the weight of the right child node. This represents the weight factor of the left subtree. This represents the weight factor of the right subtree. This represents the hash value of the parent node. The hash value of the left child node, This represents the hash value of the right child node, and || represents the concatenation operator;

[0064] Step S23: Data upload to the blockchain. The data unit uploaded to the blockchain is sent to the blockchain network as the transaction content. Nodes in the blockchain network verify and reach consensus on the transaction through a pre-selected consensus mechanism. After the transaction is approved, it is packaged into a new block and permanently recorded on the distributed ledger.

[0065] By performing the above operations, and adopting the design of batch processing user data blocks and generating dynamic weight fingerprints using weighted Merkle trees, the on-chain operation of N data packets is compressed into one, significantly reducing the on-chain frequency. By storing raw data off-chain and storing simplified information such as fingerprints on-chain, the amount of on-chain data is reduced, alleviating the pressure on blockchain transaction processing. This adapts to the monitoring needs of massive and high-frequency energy data, solving the technical problems that energy monitoring data is massive and high-frequency, and that most blockchains have limited transaction processing capabilities, high data on-chain latency, slow transaction confirmation, and difficulty in meeting real-time monitoring requirements.

[0066] Example 4, see Figure 1 and Figure 3 This embodiment is based on the above embodiment. In step S3, the data access control includes the following steps:

[0067] Step S31: Deploy a smart contract. Deploy a smart contract to manage energy monitoring data on the blockchain network. When the data unit on the chain is written into a block, the blockchain's event mechanism is triggered. The smart contract can listen to the data on the chain by subscribing to the event.

[0068] Step S32: Introduce trend momentum. When the absolute value of the trend momentum exceeds the critical threshold for multiple consecutive periods, a high-level alarm is triggered, indicating a strong and continuous deterioration trend. When the absolute value of the trend momentum exceeds the critical threshold for a single period, a normal warning is triggered. The trend momentum is calculated using the following formula:

[0069] ;

[0070] In the formula, This represents the trend momentum at time t. This represents the device reading at time t. This represents the device reading at time t-1. Represents the smoothing factor. This represents the trend momentum at time t-1;

[0071] Based on trend momentum quantification of data anomaly index, dynamic anomaly detection and tiered early warning of energy monitoring data are achieved. The formula used is as follows:

[0072] ;

[0073] In the formula, The index represents the data anomaly index, d represents the requested energy monitoring data, t represents the request time, T represents the critical threshold, and min() represents taking the minimum value.

[0074] Step S33: Set up access control logic, embed access control rules in the smart contract, and introduce real-time calculated dynamic risk scores to enable access control decisions to adaptively adjust according to the current security situation. The formula used is as follows:

[0075] ;

[0076] In the formula, This represents a dynamic risk score, where r represents the requester's role. Represents the data sensitivity coefficient. This represents the requester's credibility, dynamically calculated based on the requester's historical access behavior and compliance. The initial value is set to 0.5. This represents the weighting coefficient, used to adjust the degree of importance given to each factor;

[0077] The smart contract executes a tiered access control strategy based on the calculated dynamic risk score: it presets a high-risk threshold and a low-risk threshold. When the dynamic risk score is less than or equal to the low-risk threshold, access to the original high-precision data is allowed; when the dynamic risk score is greater than the low-risk threshold but less than the high-risk threshold, access to the anonymized data is allowed; when the dynamic risk score is greater than the high-risk threshold, the access request is rejected and the high-risk access attempt is recorded.

[0078] Step S34: Update the requester's reputation. A reputation update function is built into the smart contract and triggered at the end of each access, forming a smart risk control system that dynamically assesses the risk of data access. The formula used is as follows:

[0079] ;

[0080] ;

[0081] In the formula, This indicates the updated requester credibility. Indicates the change in creditworthiness. Indicates the reward coefficient. This represents the penalty coefficient, used to control the magnitude of changes in reputation. This indicates positive feedback. This indicates a negative punishment.

[0082] By performing the above operations, smart contracts are deployed to implement dynamic risk scoring and tiered access control. Combined with data sensitivity and requester credibility, data access permissions are restricted, and only trusted entities are authorized to obtain data at the corresponding level. Credibility is updated in real time to form a risk control closed loop, protecting energy monitoring data from the source and avoiding privacy leaks. This solves the technical problem that energy monitoring data is highly sensitive privacy information that can reflect users' living habits, whether they are at home, etc., and that inadequate data protection can easily lead to serious privacy leaks for users.

[0083] Example 5, see Figure 1 This embodiment is based on the above embodiment. In step S4, the data query verification is specifically an authorized query. The data requester initiates a data access request to the smart contract through the DApp front-end. After the smart contract verifies the permissions, it returns the corresponding dynamic weight fingerprint and content identifier. To verify the data, the requester uses the content identifier to obtain the original data from off-chain storage, calculates the hash value of the obtained original data, and compares the hash value with the dynamic weight fingerprint stored on-chain. If the two are consistent, it proves that the data has not been tampered with since it was recorded and is complete and trustworthy. If they are inconsistent, it indicates that the data has been tampered with and the trust chain is broken.

[0084] Example 6, see Figure 1 This embodiment is based on the above embodiment. In step S5, the generation of the data monitoring report specifically involves automatically generating a multi-dimensional and traceable monitoring report based on trusted energy monitoring data verified by the blockchain, combined with the abnormal warning and access control results triggered by the smart contract, so as to provide decision-making basis for energy dispatch, equipment operation and maintenance, user settlement and other businesses.

[0085] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0086] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention.

[0087] The present invention and its embodiments have been described above. This description is not restrictive, and the accompanying drawings are only one embodiment of the present invention; the actual structure is not limited thereto. In conclusion, if those skilled in the art are inspired by this description and design similar structures and embodiments without departing from the spirit of the invention, such designs should fall within the protection scope of the present invention.

Claims

1. A blockchain-based energy monitoring data storage method, characterized in that: The method includes the following steps: Step S1: Energy monitoring data acquisition, specifically, real-time acquisition of energy monitoring data from distributed energy nodes and preliminary processing; Step S2: Construct a data storage structure, specifically by storing the pre-processed energy monitoring data in an off-chain distributed storage system, constructing user data blocks containing data packets, calculating the dynamic weight of each data packet, constructing a weighted Merkle tree based on HMAC and hierarchical weights and generating dynamic weight fingerprints, forming on-chain data units and sending them to the blockchain network as transactions; Step S3: Data access control, specifically, deploying smart contracts on the blockchain network, calculating trend momentum to quantify the data anomaly index, combining data sensitivity and requester credibility to calculate a dynamic risk score, and performing tiered access control based on this score, updating the requester credibility after each access, forming an intelligent risk control system that assesses and responds to security risks in real time. Step S4: Data query verification, specifically, the data requester verifies the data credibility by comparing the hash value calculated from the on-chain dynamic weight fingerprint and the off-chain original data; Step S5: Generate a data monitoring report.

2. The energy monitoring data storage method based on blockchain according to claim 1, characterized in that: In step S2, constructing the data storage structure includes the following steps: Step S21: Off-chain storage, storing the pre-processed energy monitoring data in an off-chain distributed storage system and generating a unique content identifier; Step S22: Construct on-chain data units, construct user data blocks containing data packets, calculate the dynamic weight of each data packet, construct a weighted Merkle tree based on HMAC and hierarchical weights to generate dynamic weight fingerprints, and form structured on-chain data units containing dynamic weight fingerprints, content identifiers, timestamps, device IDs and metadata, including the following: Construct a user data block, assuming the user data block contains N data packets. Where N represents the total number of data packets, Represents a user data block; For each data packet Nonlinear adjustment is introduced to calculate dynamic weights; A weighted Merkle tree is constructed, HMAC-based structured binding is introduced, weighted leaf node hashes are calculated, and the hash values ​​of all leaf nodes are summarized into a unique root hash, which represents the dynamic weight fingerprint of the user data block and serves as the final on-chain data fingerprint. Introducing hierarchical weights, for any parent node, its weight is the weighted average of the weights of its left and right nodes, and the hash value of the parent node is determined by the hashes of its child nodes and the aggregate weight. Step S23: Data upload to the blockchain. The data unit uploaded to the blockchain is sent to the blockchain network as the transaction content. Nodes in the blockchain network verify and reach consensus on the transaction through a pre-selected consensus mechanism. After the transaction is approved, it is packaged into a new block and permanently recorded on the distributed ledger.

3. The energy monitoring data storage method based on blockchain according to claim 1, characterized in that: In step S3, the data access control includes the following steps: Step S31: Deploy a smart contract. Deploy a smart contract to manage energy monitoring data on the blockchain network. When the data unit on the chain is written into a block, the blockchain's event mechanism is triggered. The smart contract can listen to the data on the chain by subscribing to the event. Step S32: Introduce trend momentum. When the absolute value of the trend momentum exceeds the critical threshold for multiple consecutive periods, a high-level alarm is triggered; when the absolute value of the trend momentum exceeds the critical threshold once, a normal warning is triggered; calculate the trend momentum. Based on the trend momentum quantification data anomaly index, dynamic anomaly detection and hierarchical early warning of energy monitoring data can be achieved; Step S33: Set up access control logic, embed access control rules in the smart contract, and introduce dynamic risk scores calculated in real time so that access control decisions can be adaptively adjusted according to the current security situation. The smart contract executes a tiered access control strategy based on the calculated dynamic risk score: it presets a high-risk threshold and a low-risk threshold. When the dynamic risk score is less than or equal to the low-risk threshold, access to the original high-precision data is allowed; when the dynamic risk score is greater than the low-risk threshold but less than the high-risk threshold, access to the anonymized data is allowed; when the dynamic risk score is greater than the high-risk threshold, the access request is rejected and the high-risk access attempt is recorded. Step S34: Update the requester's reputation. A reputation update function is built into the smart contract and triggered at the end of each access, forming a smart risk control system that dynamically assesses the risk of data access.

4. The energy monitoring data storage method based on blockchain according to claim 1, characterized in that: In step S1, the energy monitoring data acquisition specifically involves collecting raw energy monitoring data from distributed energy nodes in real time; performing preliminary processing on the data at the edge gateway, including data cleaning and aggregation, and generating data fingerprints.

5. The energy monitoring data storage method based on blockchain according to claim 1, characterized in that: In step S4, the data query verification specifically involves an authorized query. The data requester initiates a data access request to the smart contract through the DApp front-end. After verifying the permissions, the smart contract returns the corresponding dynamic weight fingerprint and content identifier. To verify the data, the requester uses the content identifier to retrieve the original data from off-chain storage, calculates the hash value of the retrieved original data, and compares the hash value with the dynamic weight fingerprint stored on-chain. If the two match, it proves that the data has not been tampered with since it was recorded and is complete and trustworthy. If there is a discrepancy, it indicates that the data has been tampered with and the chain of trust has been broken.

6. The energy monitoring data storage method based on blockchain according to claim 1, characterized in that: In step S5, the generation of the data monitoring report specifically involves automatically generating a multi-dimensional and traceable monitoring report based on trusted energy monitoring data verified by the blockchain, combined with anomaly warnings and access control results triggered by smart contracts, to provide decision-making basis for business operations.

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