Distributed data consistency characterization and credibility verification method based on block chain

Abstract

The invention relates to a distributed data consistency characterization and credibility verification method based on a block chain, and the method comprises the steps: collecting multi-source power grid operation data in real time among a dispatching master station, a transformer substation and edge equipment, carrying out the unified structural processing of the multi-source power grid operation data, and extracting a hash abstract to guarantee the normalization of a data source; hash abstracts generated by all the nodes are uploaded to a consistency comparison engine, a multi-dimensional consistency calculation model is established based on key parameters including timestamps, voltages and currents, and data differences and conflicts are identified; state coding is carried out on a comparison result, a power grid data consistency state identifier is generated, and the state identifier is written into a block chain in combination with a device ID and a time label; and the system constructs an index mapping mechanism based on the on-chain state identifier, so that the master station and the dispatching center can perform cross-system consistency verification. According to the method, automatic consistency judgment and credible evidence storage of the distributed power grid data can be realized, and the accuracy of cross-system data sharing, the real-time performance of verification and the safety of system operation are improved.

Description

Technical Field

[0001] This invention relates to the fields of power information technology and blockchain technology, specifically to a blockchain-based distributed data consistency representation and trusted verification method. Background Technology

[0002] Against the backdrop of the rapid development of new power systems, the operation and control of power grids increasingly rely on distributed, multi-source data support. With the large-scale deployment of dispatching master stations, substation automation platforms, distribution terminals, and edge devices, a multi-level, multi-point data acquisition network has gradually formed in the power grid, covering various operational information such as voltage, current, frequency, switch status, and protection actions. The widespread access to this data provides crucial support for power grid operation situation awareness, load forecasting, and intelligent dispatching, but it also brings new challenges such as complex data sources, heterogeneous structures, and conflicting content.

[0003] In practical applications, due to issues such as communication delays, timestamp errors, and inconsistent sampling mechanisms among data acquisition devices at different nodes, discrepancies and even conflicts often arise in the descriptions of the same power grid operation event across different systems. For example, the voltage value of a certain line recorded by the dispatch center may differ from the value collected by edge acquisition devices or substation monitoring systems. This not only affects data reliability but also interferes with subsequent dispatch command execution, load control, and equipment protection. Current mainstream consistency processing methods mainly rely on centralized platforms to aggregate data or manual screening, while using techniques such as hash verification and time synchronization to partially ensure data integrity. However, these methods still have significant limitations overall.

[0004] On the one hand, the lack of unified standards among different data acquisition systems in terms of data models, communication protocols, and sampling frequencies makes traditional consistency processing methods difficult to adapt to heterogeneous systems and lacks broad applicability. On the other hand, existing consistency judgment methods often fail to fully consider the physical characteristics and business context of power grid operation. Relying solely on hash value comparisons or simple time window matching makes it difficult to effectively identify fine-grained differences, easily leading to misjudgments and omissions. Furthermore, these judgment results are mostly stored only in local systems or databases, lacking a reliable on-chain evidence storage mechanism, making it impossible to achieve verifiability and traceability of the judgment process and results, and failing to support subsequent auditing, accountability, and regulatory requirements. Summary of the Invention

[0005] The purpose of this invention is to provide a blockchain-based distributed data consistency representation and trusted verification method. This method can not only realize the automatic identification and trusted storage of distributed power grid data consistency, but also improve the accuracy of data sharing across systems, the real-time nature of verification, and the security of system operation.

[0006] To achieve the above objectives, the technical solution adopted by this invention is: a distributed data consistency representation and trusted verification method based on blockchain, comprising the following steps:

[0007] S1: Collect multi-source power grid operation data in real time between the dispatch master station, substation and edge device, and perform unified structured processing on it to extract hash digests to ensure the standardization of data sources;

[0008] S2: Upload the hash digests generated by each node to the consistency comparison engine, and establish a multi-dimensional consistency calculation model based on key parameters including timestamps, voltage and current to identify data differences and conflicts;

[0009] S3: Compare the results and encode them to generate a power grid data consistency status identifier. Combine this identifier with the device ID and time tag and write it to the blockchain to ensure that the record is tamper-proof and traceable.

[0010] S4: The system builds an index mapping mechanism based on on-chain status identifiers, which is used by the master station and dispatch center to perform cross-system consistency verification, thereby improving the reliable sharing and rapid response capabilities of power grid operation data.

[0011] Furthermore, in step S1, the unified structured processing includes field standardization, data imputation, anomaly removal, and sampling alignment operations. The field standardization uses a preset field template to standardize the expression of power grid operation data from different sources. The field template includes at least: sampling time, equipment number, monitoring type, data value, and data quality label.

[0012] Further, in step S1, the hash digest is generated for each data record after structured processing using a cryptographic hash algorithm, wherein the cryptographic hash algorithm is the SHA-256 algorithm.

[0013] Furthermore, in step S2, the multidimensional consistency calculation model integrates a time alignment mechanism, a feature parameter verification mechanism, and a business logic tolerance mechanism;

[0014] The time alignment mechanism employs a configurable sliding time window to aggregate and align the sampling times of different nodes.

[0015] The feature parameter verification mechanism is based on voltage deviation rate, current deviation rate and frequency deviation to construct a parameter feature group, and judges data consistency by combining a two-layer strategy of structural consistency and numerical tolerance.

[0016] The business logic tolerance mechanism dynamically adjusts the parameter tolerance threshold according to the power grid operation scenario.

[0017] Furthermore, the formula for calculating the voltage deviation rate is as follows:

[0018]

[0019] Among them, U i (t) and U j (t) represents the current measurements at nodes i and j at time t, respectively;

[0020] The formula for calculating the current deviation rate is:

[0021]

[0022] Among them, I i (t) and I j (t) represents the voltage measurements at nodes i and j at time t, respectively;

[0023] To evaluate the consistency of multiple parameters, a consistency comprehensive scoring function S is constructed. ij (t), used to score the consistency between nodes when multiple parameters are involved:

[0024]

[0025] Among them, w U ,w I ,w f Let w be the parameter weights. U +w I +w f =1; f i (t) and f j (t) represents the frequencies of nodes i and j at time t, respectively; Δf max To allow the maximum frequency deviation; S ij (t) represents the consistency score, S ij (t)∈[0,1]; if S ij If (t)≥0.95, then it is judged as highly consistent; if S ij If (t) < 0.8, then it is judged as obviously inconsistent; if 0.8 ≤ S ij If (t) < 0.95, it is judged to be in the middle region, which is partially consistent.

[0026] Furthermore, in step S2, the consistency comparison engine is deployed in the provincial or regional dispatch backend system, or embedded in the business scheduling module of the distribution network master station, and has real-time comparison capability and high concurrency processing capability, and supports adaptive adjustment of comparison strategy parameters according to power grid load fluctuations.

[0027] Further, in step S3, the status encoding encapsulates the comparison result into a structured status record, which includes at least: status number, time tag, list of participating nodes, consistency score, status type, and comparison model version number; the status type includes consistent, partially consistent, and abnormal.

[0028] The power grid data consistency status identifier is generated by performing a hash operation on the structured status record. The input fields of the hash operation include: status number, time tag, list of participating nodes, consistency score, status type, and comparison model version number.

[0029] Furthermore, in step S3, the writing to the blockchain adopts a consortium blockchain architecture, and the on-chain transaction is triggered by a smart contract. The consensus protocol of the blockchain is Byzantine Fault Tolerance (PBFT) or Proof-of-Stake (PoS). In the blockchain, multiple state identifiers within the same comparison period are organized through a Merkle tree structure, and the root node of the Merkle tree is written into the block header of the corresponding block.

[0030] Furthermore, in step S4, the index mapping mechanism is a bidirectional index table between on-chain state identifiers and off-chain original data nodes. The bidirectional index table includes state hash, timestamp, comparison node, state type, and off-chain storage address. The cross-system consistency verification achieves efficient verification without transmitting original data by retrieving the on-chain state hash to reverse locate the data source and consistency conclusion.

[0031] Furthermore, in step S4, the index mapping mechanism also includes a role-based access control model (RBAC), which dynamically binds the range of queryable data according to the caller's role and ensures the traceability of operations through an access token mechanism and interface call log recording.

[0032] Compared with existing technologies, this invention has the following advantages: This invention proposes a blockchain-based distributed data consistency representation and trusted verification method, possessing both engineering practicality and structural completeness. This method achieves standardized expression and unique identification of multi-source power grid operation data through a unified data structure template and hash digest algorithm. During the consistency calculation process, a multi-dimensional comparison model is constructed, integrating key factors such as time synchronization error, voltage and current deviation rate, and sampling delay to form a structured state scoring mechanism. The comparison results are used to generate state identifiers using encrypted hashing, and blockchain technology is used for immutable on-chain storage, ensuring the records are verifiable and traceable. The system establishes a bidirectional index mapping between on-chain states and off-chain node information, enabling rapid state-level querying and cross-platform consistency verification. The accompanying role access control and batch query mechanism improve data retrieval efficiency while ensuring security and controllability, making it suitable for data coordination, state verification, and operation and maintenance decision support among multi-level power grid systems. Attached Figure Description

[0033] Figure 1 This is a flowchart illustrating the implementation of the blockchain-based distributed data consistency representation and trusted verification method provided in this embodiment of the invention.

[0034] Figure 2 This is a histogram of the state consistency score distribution in an embodiment of the present invention. Detailed Implementation

[0035] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0036] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of this application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0037] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this application. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0038] like Figure 1 As shown, this embodiment provides a blockchain-based distributed data consistency representation and trusted verification method, characterized by the following steps:

[0039] S1: Collect multi-source power grid operation data in real time between the dispatch master station, substation and edge device, and perform unified structured processing on it to extract hash digests to ensure the standardization of data sources;

[0040] S2: Upload the hash digests generated by each node to the consistency comparison engine, and establish a multi-dimensional consistency calculation model based on key parameters including timestamps, voltage and current to identify data differences and conflicts;

[0041] S3: Compare the results and encode them to generate a power grid data consistency status identifier. Combine this identifier with the device ID and time tag and write it to the blockchain to ensure that the record is tamper-proof and traceable.

[0042] S4: The system builds an index mapping mechanism based on on-chain status identifiers, which is used by the master station, dispatch center and other systems to perform cross-system consistency verification, thereby improving the reliable sharing and rapid response capabilities of power grid operation data.

[0043] The relevant content involved in this method will be further detailed below.

[0044] S1: Collect multi-source power grid operation data in real time between the dispatch master station, substation and edge device, and perform unified structured processing to extract hash digests to ensure the standardization of data sources.

[0045] In step S1, a unified structured data template is first designed to standardize the representation of operational data from different sources in the power grid. This template includes fields such as sampling time, device number, parameter type (e.g., voltage, current, frequency), data value, and quality label, and is suitable for real-time operational data from various nodes, including the dispatch master station, substation automation systems, and edge intelligent devices. This structured mechanism ensures the comparability of data collected from different systems, providing a unified input basis for subsequent consistency modeling.

[0046] In the implementation of this invention, data acquisition and standardization processing modules are first deployed among typical system nodes at different levels of the power grid to complete the unified formatted expression of multi-source power grid operation data and generate data summaries to support subsequent consistency comparison.

[0047] The unified structured processing includes field standardization, data imputation, anomaly removal, and sampling alignment operations. The field standardization uses a preset field template to standardize the expression of power grid operation data from different sources. The field template includes at least: sampling time, equipment number, monitoring type, data value, and data quality label.

[0048] The nodes include, but are not limited to: the dispatch master station, the substation monitoring system, and edge acquisition devices (such as distribution terminals, transformer area monitoring devices, etc.). These nodes collect real-time operational data within their respective areas of responsibility through existing SCADA systems, protection and control devices, or smart terminals.

[0049] Because the data acquisition methods, sampling frequencies, and parameter types differ among nodes, this invention proposes a unified data structure template that maps data from different sources to a standard set of fields, ensuring comparability between data. The field design is shown in the table below:

[0050]

[0051]

[0052] Using the above structural template, all data collected by the systems is converted into data packets of the same format before entering the consistency judgment module, which effectively improves the operability of data fusion and comparison between different systems.

[0053] After structuring, each data record is processed using a hash algorithm to generate a data digest. This digest is irreversible and unique, enabling it to be used for subsequent consistency calculations and preventing malicious modification of data during transmission. The data digest does not contain the original value; it is used only to uniquely identify the record, thus ensuring the trustworthiness of the data source while reducing communication bandwidth and storage pressure.

[0054] S2: Upload the hash digests generated by each node to the consistency comparison engine, establish a multi-dimensional consistency calculation model based on key parameters such as timestamp, voltage, and current, and identify data differences and conflicts.

[0055] In step S2, after the data sources have completed structured processing, this invention introduces a multi-dimensional consistency modeling mechanism based on power grid business characteristics. This model comprehensively considers factors such as time synchronization deviation, electrical parameter fluctuation tolerance, node geographical topology, and measurement delay. It employs a sliding time window strategy and a feature vector matching algorithm to align and compare data collected from multiple nodes. The comparison results not only determine whether the data is consistent but also quantify the level and reliability of consistency, outputting standardized consistency status results (such as completely consistent, partially consistent, significantly inconsistent, etc.).

[0056] After completing the structured processing and hash digest generation in step S1, the digest data generated by the dispatch master station, substation system, and edge acquisition devices are uploaded to the consistency comparison engine on the central side via internal network or dedicated power communication channel. This engine can be deployed in the provincial or regional dispatch backend system, or embedded in the business dispatch module of the distribution network master station, and has real-time comparison capability and high concurrency processing capability.

[0057] Considering the characteristics of power grid data, such as high temporal sequence, wide spatial distribution, and inconsistent sampling frequency, this invention integrates the following three mechanisms in the construction of the multidimensional consistency calculation model: time alignment mechanism, feature parameter verification mechanism, and business logic tolerance mechanism, in order to improve the accuracy and robustness of the judgment.

[0058] 1. Sliding time window alignment mechanism

[0059] Due to issues such as time drift, network transmission latency, and buffering delays in sampling from different nodes in the power grid, even when collecting data on the same physical event, the sampling time may differ by milliseconds. To address this, this invention establishes a configurable sliding time window, with a default value of ±100 milliseconds, to aggregate and align data uploaded from different nodes along the time dimension. This mechanism ensures that the comparison engine compares "observations within the same time period" rather than "time-displaced data," thus avoiding misjudgments.

[0060] In addition, the system supports adaptive adjustment of the sliding window width based on the severity of load fluctuations. For example, in scenarios such as peak shaving and frequency disturbances, the sliding window can be tightened to ±50 milliseconds to improve response sensitivity.

[0061] 2. Multi-parameter feature fusion and comparison mechanism

[0062] Different measurement types of structured data (such as voltage, current, and frequency) have different physical characteristics, so using a uniform comparison logic is prone to errors. To address this, this invention refines the comparison logic into parameter feature groups and uses a two-layer strategy of "structural consistency + numerical tolerance" for judgment.

[0063] Specifically, the first step is to compare the data summaries of each node. If the summaries are completely identical, the measurement data are considered to be consistent, and no further analysis is needed. If the summaries are different, proceed to the second step, where a detailed comparison is performed according to the following dimensions.

[0064] The formula for calculating the voltage deviation rate is:

[0065]

[0066] Among them, U i (t) and U j (t) represent the current measurements at nodes i and j at time t, respectively. If δ U (t)≤∈ U That is, considered consistent, ∈ U This is the tolerance threshold, such as 1.5%.

[0067] The formula for calculating the current deviation rate is:

[0068]

[0069] Among them, I i (t) and I j (t) represents the voltage measurements at nodes i and j at time t, respectively.

[0070] The frequency consistency judgment satisfies:

[0071] |f i (t)-f j (t)|≤Δf max (3)

[0072] Where, Δf max To allow for the maximum frequency deviation, it is generally set to 0.05Hz or 0.1Hz.

[0073] To comprehensively reflect the consistency of multiple indicators, this invention further designs a consistency comprehensive scoring function S. ij (t), used to score the overall consistency among nodes when multiple parameters are involved:

[0074]

[0075] Among them, w U ,w I ,w f Let w be the parameter weights.U +w I +w f =1; f i (t) and f j (t) represents the frequencies of nodes i and j at time t, respectively; Δf max To allow the maximum frequency deviation; S ij (t) represents the consistency score, S ij (t)∈[0,1], a higher value indicates better consistency; the system default consistency score threshold is 0.95, if S ij If (t)≥0.95, then it is judged as highly consistent; if S ij If (t) < 0.8, then it is judged as obviously inconsistent; if 0.8 ≤ S ij If (t) < 0.95, it is judged to be in the middle region, which is partially consistent.

[0076] 3. Consistency State Classification and Traceable Output

[0077] The final output of the comparison results will be categorized into the following three states. The system will generate a structured state report for each comparison. The state categories are as follows:

[0078]

[0079] To support dispatchers in tracing and identifying the source of problems, the system also records metadata such as the comparison window number, participating node pairs, comparison strategy version, and model parameter thresholds in the comparison results. This supports subsequent strategy backtracking and behavior analysis, and is particularly valuable for monitoring tasks involving large-scale distributed power sources or photovoltaic disturbance areas.

[0080] S3: Compare the results and encode them to generate a power grid data consistency status identifier. Combine this identifier with the device ID and time stamp and write it to the blockchain to ensure that the record is tamper-proof and traceable.

[0081] In step S3, the present invention combines key information such as the state result output by the comparison engine, participating node information, and timestamps to generate a unique state hash identifier. This state information is written into the blockchain system via a smart contract mechanism and organized and managed using a Merkle tree structure to ensure that each consistency record possesses immutability, temporal traceability, and network consensus confirmation capabilities. This step constructs the core mechanism of "data consistency state on-chain," providing infrastructure for trusted data storage in the power system.

[0082] After completing the consistency comparison, the system generates a set of result data based on the comparison relationship of each node, including consistency score, consistency state category, list of participating nodes, sampling time window number, comparison model version number, etc. To achieve tamper-proof recording and efficient verification of subsequent states, this invention designs a state identifier generation and on-chain evidence storage mechanism, including three sub-processes: state structure encoding, state hash generation, and blockchain writing.

[0083] 1. Consistent State Structured Coding

[0084] The core result of the comparison output will be encapsulated into a structured status record. Its fields are as follows:

[0085]

[0086]

[0087] These fields, defined in a standardized format, will serve as the core input for the on-chain state summary. State numbers are generated using a combination of "event type_timestamp" to ensure uniqueness. The node list supports comparison scenarios from two or more data sources and can be extended to heterogeneous data verification between edge devices and the scheduling master station.

[0088] 2. State hash generation and digest compression

[0089] To ensure that the state record has non-repudiation and fixed representation before being uploaded to the blockchain, this invention uses a cryptographic hash algorithm (e.g., SHA-256) to concatenate and digest the structured state fields in a predetermined order to generate the corresponding state identifier code (Status Hash).

[0090] The state hash value is defined as:

[0091] H state =Hash(ID||Time||Nodes||Score||StateType||Version) (5)

[0092] The "|" symbol indicates a field concatenation operation. All fields are converted to strings before concatenation, and redundant spaces or separators are removed to ensure that the same state generates the same hash value on different nodes.

[0093] This state hash possesses significant characteristics such as uniqueness, irreversibility, and compactness. Uniqueness means that even a minor change in any field will result in a significant change in the hash value, ensuring that each comparison record is independent and unforgeable. Irreversibility means that the original comparison content cannot be reconstructed from the hash value, effectively protecting the business sensitivity and data privacy of the comparison details. Compactness ensures that the hash value has a fixed length, suitable for efficient storage and rapid matching verification in the blockchain. Furthermore, to improve the efficiency of organizing multiple states on the chain, the system also generates a corresponding hash batch number for each round of comparison tasks. Through a batch grouping mechanism, multiple state hashes are uniformly organized into blocks, further optimizing on-chain transaction processing performance and structure management.

[0094] 3. Blockchain-based state storage mechanism

[0095] Each consistent state hash will be written into the blockchain system as an on-chain transaction when triggered by a smart contract. This invention supports deployment using a consortium blockchain architecture, with typical scenarios including trusted data storage among multiple levels of nodes within a power grid enterprise, from provincial dispatch centers to regional dispatch centers to station control centers to terminals.

[0096] To further improve verification efficiency and save on-chain storage resources, this invention employs a Merkle tree structure to batch organize all state hashes. Within a comparison period (e.g., every 5 minutes), all state hashes are used as leaf nodes to generate a Merkle tree, and its root node (Merkle Root) is written into the block header of the current block. The complete on-chain format of each state record is as follows:

[0097]

[0098] Smart contracts automatically verify elements such as the integrity of the state structure, the legality of the upload time, and the signature permissions, and record call event logs to ensure the auditability and security integrity of on-chain data.

[0099] S4: The system builds an index mapping mechanism based on on-chain status identifiers, which is used by the master station, dispatch center and other systems to perform cross-system consistency verification, thereby improving the reliable sharing and rapid response capabilities of power grid operation data.

[0100] In step S4, to support rapid cross-domain consistency verification between power grid systems, this invention further constructs a mapping index mechanism between status identifiers and original data nodes. The scheduling system, master station platform, or third-party regulatory body can retrieve on-chain status hashes to reverse-locate the data source, time period, and consistency conclusion, achieving efficient verification without transmitting original data. This index mechanism supports applications such as rapid verification, batch auditing, and visual traceability, providing crucial support for ensuring the reliable sharing and collaborative operation of power grid data.

[0101] After generating and storing the consistency state identifier on the blockchain, in order to realize data consistency verification, historical event tracing, and scheduling strategy auxiliary decision-making among various business systems, this invention constructs a fast retrieval index mechanism and cross-domain verification service structure based on on-chain state hashing, which supports trusted interaction between the main platform, scheduling center, control cloud platform, and third-party regulatory system.

[0102] The core idea of ​​this mechanism is to achieve a low-latency, highly reliable, and clearly structured cross-system consistent call architecture by combining on-chain evidence storage, off-chain mapping indexes, and permission-controlled verification interfaces.

[0103] 1. Design of a combined on-chain and off-chain index structure

[0104] While writing state hashes to the blockchain, the system automatically builds an off-chain index table to associate each state hash with detailed comparison information, including the list of participating nodes, parameter types, comparison window numbers, and storage paths.

[0105]

[0106]

[0107] To improve query efficiency, the index structure combines key-value mapping with a memory caching mechanism. A bidirectional mapping between the hash primary key and path value is automatically generated during index creation, allowing upper-layer systems to directly access the corresponding comparison results through state hashes. The index supports time window archiving, node partitioning, and batch number aggregation, exhibiting strong scalability.

[0108] 2. Cross-system verification and invocation process

[0109] The system is designed with a unified verification service interface, allowing different systems such as regulation, operation and maintenance, and supervision to initiate cross-domain queries through status identifiers. The query process uses the status hash as the entry point, first calling the smart contract interface to verify whether the hash has been written to the ledger, and confirming the validity of its Merkle path through the block header index; then it enters the off-chain index layer, and uses the hash key value to look up its comparison window number, participating nodes, score value, and status conclusion, among other core indicators. The verification process does not require the transmission of raw data; it only needs to match the status hash with the on-chain structure to complete rapid rights confirmation.

[0110] Furthermore, to enhance the mathematical provability of the verification, this invention defines the following state consistency determination function V(H):

[0111]

[0112] When V(H) = 1, it means that the state hash does exist in the blockchain and the off-chain index structure matches it, so the state can be considered trustworthy and traceable; otherwise, the query request will be rejected or marked as invalid.

[0113] 3. Access control and interface security mechanisms

[0114] To ensure the legitimacy and security of data accessed by systems at all levels, the system introduces a Role-Based Access Control (RBAC) model. When requesting status verification services, callers must first authenticate themselves, and the system dynamically binds the scope of data they can query based on their role information. For example, the provincial dispatch system can access data from the entire network, the regional dispatch system is limited to the status of devices within its jurisdiction, and third-party auditors only have read-only permissions to freeze batches. Furthermore, the system employs an access token mechanism and API call logging to ensure traceability and non-repudiation capabilities. All data transmission uses HTTPS encrypted channels and incorporates a dual verification mechanism of timestamps and random numbers to prevent replay attacks.

[0115] 4. Rapid response mechanism and system performance optimization

[0116] To address the demands of high-frequency data access and batch verification, this invention introduces a multi-level caching mechanism and batch processing interface into the index query structure. The status index is sharded and stored between local disk and memory, with hot data residing in memory and cold data pages being updated periodically, thereby improving query hit rate. For large-scale comparison scenarios, the system also provides an aggregation interface to support simultaneous queries of multiple status hashes and returns auxiliary diagnostic tools such as comparison batch trend statistics and node deviation heatmaps. Figure 2 The diagram shown is a state comparison score distribution map, which can be used to help identify whether a node has deviated from the group trend for a long time.

[0117] Figure 2 In the diagram, the horizontal axis represents the status score range, and the vertical axis represents the number of statuses within that range. Abnormal score areas (e.g., <0.85) are highlighted. Furthermore, the system supports a comparison cache pool mechanism, preloading frequently accessed comparison groups from the past 10 minutes into the cache, significantly improving interface response performance and reducing database access frequency.

[0118] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0119] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0120] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0121] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0122] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1.A blockchain-based distributed data consistency characterization and trusted verification method, characterized in that, The method comprises the following steps: S1: Real-time collection of multi-source power grid operation data among dispatching master stations, substations and edge devices, and unified structured processing thereof, extraction of a hash digest to guarantee data source specification; S2: Upload of the hash digest generated by each node to a consistency comparison engine, establishment of a multi-dimensional consistency calculation model based on key parameters including a timestamp, voltage and current, identification of data differences and conflicts; S3: State coding of the comparison results, generation of a power grid data consistency state identifier, and writing into a blockchain in combination with a device ID and a time label to ensure that the record is tamper-proof and traceable; S4: Construction of an index mapping mechanism based on the on-chain state identifier for cross-system consistency verification by the master stations and dispatching centers, and improvement of power grid operation data trusted sharing and rapid response capability. 2.The blockchain-based distributed data consistency characterization and trusted verification method according to claim 1, characterized in that, In step S1, the unified structured processing comprises field standardization, data filling, exception elimination and sampling alignment operations, the field standardization adopts a preset field template to standardize the expression of power grid operation data from different sources, and the field template at least includes: sampling time, device number, monitoring type, data value and data quality label. 3.The blockchain-based distributed data consistency characterization and trusted verification method according to claim 1, characterized in that, In step S1, the hash digest is generated by an encryption hash algorithm for each data record after structured processing, and the encryption hash algorithm is SHA-256 algorithm. 4.The blockchain-based distributed data consistency characterization and trusted verification method according to claim 1, characterized in that, In step S2, the multi-dimensional consistency calculation model fuses a time alignment mechanism, a feature parameter verification mechanism and a business logic tolerance mechanism; The time alignment mechanism adopts a configurable sliding time window for aggregating and aligning the sampling times of different nodes; The feature parameter verification mechanism constructs a parameter feature group based on voltage deviation rate, current offset rate and frequency deviation, and judges data consistency through a double-layer strategy combining structural consistency and numerical tolerance; The business logic tolerance mechanism dynamically adjusts the parameter tolerance threshold according to the power grid operation scene. 5.The blockchain-based distributed data consistency characterization and trusted verification method according to claim 4, characterized in that, The calculation formula of the voltage deviation rate is: where U i (t) and U j (t) are the current measurements of nodes i and j at time t, respectively. The calculation formula of the current deviation rate is: where I i (t) and I j (t) are the voltage measurements at nodes i and j at time t, respectively. To evaluate the consistency degree of multiple parameters, a consistency comprehensive score function S is constructed ij (t), to score the consistency between nodes when multiple parameters participate: where w U ,w I ,w f are parameter weights, satisfying w U +w I +w f =1; f i (t) and f j (t) are the frequencies of nodes i and j at time t, respectively; Δf max is the maximum frequency deviation allowed; S ij (t) is the consistency score, S ij (t) ∈ [0, 1]; if S ij (t) ≥ 0.95, it is judged as highly consistent; if S ij (t) < 0.8, it is judged as obviously inconsistent; if 0.8 ≤ S ij (t) < 0.95, it is judged as in the intermediate region, partially consistent. 6.The blockchain-based distributed data consistency characterization and trusted verification method according to claim 1, wherein, In step S2, the consistency comparison engine is deployed in the provincial or regional dispatching background system, or embedded in the business dispatching module of the distribution network master station, has real-time comparison capability and high concurrency processing capability, and supports adaptive adjustment of comparison strategy parameters according to power grid load fluctuation. 7.The blockchain-based distributed data consistency characterization and trusted verification method according to claim 1, wherein, In step S3, the state coding encapsulates the comparison results into a structured state record, and the structured state record at least includes: state number, time label, participating node list, consistency score, state type, comparison model version number; the state type includes consistent, partially consistent and abnormal; The power grid data consistency state identifier is generated by hash operation on the structured state record, and the input fields of the hash operation include: state number, time label, participating node list, consistency score, state type, comparison model version number. 8.The blockchain-based distributed data consistency characterization and trusted verification method according to claim 1, wherein, In step S3, the writing blockchain adopts a consortium chain architecture, triggers a chain transaction through a smart contract, and uses a Byzantine fault tolerance algorithm (PBFT) or a proof of stake mechanism (PoS) as a consensus protocol of the blockchain; in the blockchain, multiple state identifiers in the same comparison period are organized through a Merkle tree structure, and a root node of the Merkle tree is written into a block header of a corresponding block. 9.The blockchain-based distributed data consistency characterization and trusted verification method according to claim 1, wherein, In step S4, the index mapping mechanism is a bidirectional index table of the on-chain state identifier and the off-chain original data node, the bidirectional index table includes a state hash, a time label, a comparison node, a state type, and an off-chain storage address; the cross-system consistency verification realizes efficient verification without transmitting original data by searching the on-chain state hash to reversely locate a data source and a consistency conclusion. 10.The blockchain-based distributed data consistency characterization and trusted verification method according to claim 9, characterized in that, In step S4, the index mapping mechanism further includes a role-based access control model (RBAC), the access control model dynamically binds a queryable data range according to a role of a calling party, and ensures operation traceability through an access token mechanism and an interface calling log record.

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