CN-122020081-A - Cross checking method based on blockchain electric quantity data

CN122020081ACN 122020081 ACN122020081 ACN 122020081ACN-122020081-A

Abstract

The invention discloses a block chain-based electric quantity data cross checking method, which relates to the technical field of electric quantity data processing and comprises the following steps of collecting electric quantity data of all nodes in a power grid system, wherein the nodes comprise power generation nodes, power utilization nodes and energy storage nodes; the method comprises the steps of carrying out real-time anomaly detection on electric quantity data of each node by adopting a space-time correlation analysis algorithm based on a preset electric quantity fluctuation upper limit value, sending a quantity re-acquisition instruction to an anomaly node through a monitoring platform when anomaly is detected, acquiring electric quantity data of the anomaly node again, starting difference adjustment operation if original electric quantity data of the anomaly node is uploaded to a block chain distributed account book, generating difference adjustment data record and uploading the difference adjustment data record to a block chain for storage, establishing a cross check network among the nodes based on a preset intelligent contract rule, and carrying out cross check on the electric quantity data of each node. The invention is based on block chain, and realizes the monitoring and correction of electric quantity data through anomaly detection and cross check.

Inventors

CHEN MINGMING
MA JIKE
JIANG MING
PEI ZIXIA
XU WEI
CHEN JINING
XIA YUHANG
SHAN CHAO
XU HAIYANG
ZHOU SHENGCUN

Assignees

国网江苏省电力有限公司营销服务中心
江苏方天电力技术有限公司

Dates

Publication Date: 20260512
Application Date: 20251222

Claims (10)

1. The block chain-based electric quantity data cross checking method is characterized by comprising the following steps of: step S1, collecting electric quantity data of each node in a power grid system, wherein each node comprises a power generation node, a power utilization node and an energy storage node, and each node has unique space coordinates; step S2, carrying out real-time anomaly detection on the electric quantity data of each node by adopting a space-time correlation analysis algorithm based on a preset electric quantity fluctuation upper limit value; Step S3, when abnormality is detected, transmitting a power re-acquisition instruction to the abnormal node through the monitoring platform, and re-acquiring power data of the abnormal node; s4, if the original electric quantity data of the abnormal node is uploaded to the block chain distributed account book and cannot be modified, a difference adjustment operation is started, a difference adjustment data record is generated, and the difference adjustment data record is uploaded to the block chain for storage; And S5, establishing a cross check network among the nodes based on a preset intelligent contract rule, and performing cross check on the electric quantity data of each node to continuously identify the electric quantity data abnormality.
2. The method according to claim 1, wherein in the step S2, the abnormality detection step specifically includes: calculating the expected electric quantity of each node at a specific moment; calculating absolute deviation between actual electric quantity readings and expected electric quantity values to obtain fluctuation values; If the fluctuation value is larger than the electric quantity fluctuation upper limit value, judging that the electric quantity data of the node is abnormal; The power fluctuation upper limit value is set based on the node historical operation data and the phase offset of the node i.
3. The method of claim 2, wherein the calculation of the expected electrical quantity uses a modified space-time weighting algorithm, and the specific calculation formula is: Wherein, the Representing the expected power of node i at time t, Representing the set of neighbor nodes of node i, Representing the time-space weight factor of the system, A time-decay factor is represented and, Representing the power reading of neighbor node j at time t, Representing the power reading of neighbor node j at time t-1, The temperature influence coefficient is represented by a temperature coefficient, The amount of temperature change, i.e., the difference between the current temperature of node i and the historical average temperature, is represented.
4. The method of claim 3, wherein the set of neighbor nodes The determining method of (1) comprises the following steps: setting a spatial distance threshold value, wherein the value of the spatial distance threshold value is ; Calculating the actual geographic distance between the nodes based on the space coordinate information of the nodes; screening all nodes with the actual geographic distance smaller than the spatial distance threshold value from the target node i to form a neighbor node set 。
5. The method according to claim 4, wherein in the step S4, the step of adjusting the difference specifically includes the steps of: Calculating an adjustment amount, wherein the adjustment amount is a difference value between the re-collected electric quantity data and the original abnormal reading; Generating a difference adjustment record comprising a difference adjustment amount, a difference adjustment time and a difference adjustment reason; and uploading the difference adjustment record to a blockchain network in an encrypted transaction mode and establishing an associated index of the original data and the difference adjustment data in the blockchain.
6. The method according to claim 5, wherein in step S4, the difference adjustment data and the power raw data are stored in a blockchain by transaction hash association.
7. The method according to claim 6, wherein in the step S5, the cross check specifically includes the steps of: Defining a plurality of check groups according to a power grid topological structure, wherein each check group comprises a group of associated nodes and a corresponding total electricity quantity value; for each check group, calculating algebraic sum of electric quantity readings of all nodes in the group; Calculating the absolute deviation between the sum of the node electric quantity readings in the group and the total electric quantity value; If the deviation is larger than the allowable deviation threshold, judging that the check group data is abnormal; For the abnormal check group, starting an abnormal node positioning algorithm to identify a specific problem node; Wherein the allowable deviation threshold is set based on grid physical characteristics and operational experience.
8. The method according to claim 7, wherein the abnormal node location algorithm specifically comprises the steps of: Calculating the abnormal contribution degree of each node in each check group to which the node belongs; sequencing all nodes according to the abnormal contribution degree from high to low; Selecting a node with the abnormal contribution degree exceeding a preset abnormal threshold as an abnormal node; Starting a difference adjustment process for the identified abnormal node, and re-acquiring and verifying electric quantity data; the preset abnormal threshold is determined based on the average value and standard deviation of all node abnormal contribution degrees.
9. The method of claim 8, wherein the anomaly contribution is calculated using the formula: Wherein, the Represents the abnormal contribution of node i in the check group G, Representing the actual power reading of node i, Representing the expected electrical quantity value of node i, Indicating the total deviation value of the check group G, Representing the spatial isolation of node i, i.e. the average distance from other nodes in the group, The spatial distribution standard deviation of the check group G is shown.
10. The method according to claim 9, wherein in the step S5, the executing process of the smart contract includes the steps of: automatically triggering a checking flow through an event-driven mechanism, and automatically executing a preset cross checking step by the intelligent contract when the new electric quantity data is detected to be uplink; generating a difference adjustment instruction according to the checking result, and distributing the difference adjustment instruction to related nodes through a blockchain network; And recording complete checking process data, including checking time, participating nodes, checking results and execution states, and forming an untampereable audit trail.

Description

Cross checking method based on blockchain electric quantity data Technical Field The invention relates to the technical field of electric quantity data processing, in particular to an electric quantity data cross checking method based on a block chain. Background In the dispatching planning of the power system, a short-term power generation planning method taking safety constraint dispatching as a core technology is widely applied to various power dispatching control centers. With the rapid development of smart grids and distributed energy sources, the number of nodes in a grid system is increased sharply, and the nodes comprise photovoltaic power generation, wind power generation, an energy storage system, various power loads and the like. The electric quantity data generated by the nodes are important basis for monitoring the operation of the power grid, accounting and diagnosing faults. However, due to equipment aging, communication interference, environmental factors and the like, abnormality often occurs in the process of acquiring electric quantity data, so that the data quality is reduced. Therefore, a blockchain-based electric quantity data cross checking method is needed, and aims to ensure the accuracy and reliability of the electric quantity data through an intelligent anomaly detection and cross checking mechanism. The invention discloses a annual energy check calculation method, which is disclosed in China patent with publication number CN114548603A, and specifically comprises the steps of determining a power grid range, a calculation period and other calculation boundaries of annual energy check calculation to be carried out, carrying out multi-period decomposition and dimension reduction processing on power grid annual calculation data, decomposing an annual energy check calculation model into a plurality of sub-models, circularly calculating each sub-model, carrying out close period merging analysis, reducing the number of time periods entering optimization, taking the connection of unit combination states among the sub-models into consideration, establishing a SCUC dimension reduction model after the time period merging, solving to obtain a unit combination result, establishing a full-period SCED model based on the unit combination result, solving to obtain a power plant electric quantity optimization result under each sub-model, and completing the annual energy check calculation based on multi-period decomposition and dimension reduction. The annual energy verification calculation method accurately considers the influence of practical constraint on the power generation capacity of the power plant, and improves the accuracy and calculation efficiency of annual energy verification results of the power plant. Disclosure of Invention Aiming at the defects of the prior art, the invention provides a block chain-based electric quantity data cross checking method. In order to achieve the above purpose, the invention adopts the following technical scheme: a block chain-based electric quantity data cross checking method comprises the following steps: step S1, collecting electric quantity data of each node in a power grid system, wherein each node comprises a power generation node, a power utilization node and an energy storage node, and each node has unique space coordinates; step S2, carrying out real-time anomaly detection on the electric quantity data of each node by adopting a space-time correlation analysis algorithm based on a preset electric quantity fluctuation upper limit value; Step S3, when abnormality is detected, transmitting a power re-acquisition instruction to the abnormal node through the monitoring platform, and re-acquiring power data of the abnormal node; s4, if the original electric quantity data of the abnormal node is uploaded to the block chain distributed account book and cannot be modified, a difference adjustment operation is started, a difference adjustment data record is generated, and the difference adjustment data record is uploaded to the block chain for storage; And S5, establishing a cross check network among the nodes based on a preset intelligent contract rule, and performing cross check on the electric quantity data of each node to continuously identify the electric quantity data abnormality. Further, in the step S2, the abnormality detection step specifically includes: calculating the expected electric quantity of each node at a specific moment; calculating absolute deviation between actual electric quantity readings and expected electric quantity values to obtain fluctuation values; If the fluctuation value is larger than the electric quantity fluctuation upper limit value, judging that the electric quantity data of the node is abnormal; The power fluctuation upper limit value is set based on the node historical operation data and the phase offset of the node i. Further, the calculation of the expected electric quantity adopts an improved space-time weighting a