Intelligent management method for power marketing archives

Abstract

The invention provides a power marketing archive intelligent management method, which comprises the steps of receiving archive data through a power data acquisition interface, matching an electricity price feature code in an archive based on a real-time electricity price policy identifier, and injecting a version calibration mark into unmatched data; constructing an electricity consumption abnormity prediction model based on the user historical load mode and the environmental factors; when a query request is responded, dynamic penalty parameters are generated based on the user behavior portrait and the rule base; a quantum key distribution path is generated based on a power grid topological structure, and cascade encryption is implemented along physical connection nodes; dynamically associating user operation authority with a power grid operation risk signal, and automatically disabling core operation in a high-risk state; and analyzing archive data in a security isolation environment to drive line loss optimization, electric charge tracing and fault pre-judgment service flow in real time, and outputting feedback archive update based on the service. According to the invention, the safety, reliability and operation efficiency of power marketing archive management are improved.

Description

Technical Field

[0001] This invention relates to the field of information management technology for power marketing, and more specifically, to an intelligent management method for power marketing records. Background Technology

[0002] Traditional power marketing record management suffers from numerous technical bottlenecks: Regarding record management efficiency, existing systems primarily rely on manual operation for record storage, retrieval, and verification, which is not only time-consuming and labor-intensive but also prone to errors due to human factors, leading to archiving chaos or data omissions. Particularly when electricity pricing policies are updated, the lack of an automated matching mechanism often results in the electricity price feature codes in the records failing to be updated in a timely manner, causing errors in electricity bill calculations. In terms of data security, decentralized record systems struggle to achieve unified protection, and the coexistence of paper and electronic records easily creates information silos, posing risks of data loss and information leakage.

[0003] Meanwhile, the existing system lacks effective real-time interception methods for abnormal electricity bill modifications, and the security protection of mobile terminal data transmission is also relatively weak. In terms of business collaboration, there is a significant disconnect between changes to archived data and the response of business systems; important events such as line parameter adjustments and changes in electricity pricing policies cannot trigger real-time business optimization processes such as line loss analysis and electricity bill tracing. Furthermore, data exchange between the archived system and other business systems such as enterprise resource planning and customer relationship management suffers from inconsistent formats and incompatible interfaces, severely hindering cross-system collaboration efficiency. These technical deficiencies not only affect the work efficiency of electricity marketing operations but also negatively impact the quality of power supply services and management level. Summary of the Invention

[0004] The purpose of this application is to provide an intelligent management method and system for power marketing records, which has the advantages of improving record management efficiency, enhancing data security protection, and realizing business collaboration.

[0005] This application provides an intelligent management method for electricity marketing archives, with the following technical solution: Archive data is received through an electricity data acquisition interface; electricity price feature codes in the archives are matched based on real-time electricity price policy identifiers; version calibration tags are injected into unmatched data; when an electricity bill modification operation is performed, an electricity consumption anomaly prediction model is constructed based on the user's historical load pattern and environmental factors; a distributed ledger locking mechanism is triggered when the modified value deviates from the prediction range; when responding to query requests, dynamic default penalty parameters are generated based on user behavior profiles and a rule base; during mobile data transmission, a quantum key distribution path is generated based on the power grid topology, and cascade encryption is implemented along physical connection nodes; user operation permissions are dynamically associated with power grid operation risk signals, and core operations are automatically disabled in high-risk states; in a secure isolation environment, archive data is parsed to drive real-time line loss optimization, electricity bill tracing, and fault prediction business flows, and the business output is used to feed back into archive updates.

[0006] Furthermore, this application also proposes that receiving archival data includes: triggering an archive scanning engine based on electricity price policy update signals; identifying the topological coordinates of metering points through a spatial geocoding converter; mapping the coordinates to a regional electricity price feature matrix; creating a spatiotemporal calibration mark with a version traceability chain using a timestamp generator; and constructing a tamper-resistant storage array using a Merkle tree structure to write the calibration data into the storage array.

[0007] Furthermore, this application proposes that triggering the distributed ledger locking mechanism includes: constructing a spatiotemporal convolutional neural network model; generating a predicted boundary surface through model inference; activating an operation freeze command when the modified value crosses the surface; generating a distributed ledger evidence package based on the operator's biometrics and the hash of the changed value; performing multi-dimensional collaborative verification, including power grid status verification, user behavior verification, and change trajectory verification; and generating a lock release signal and triggering an operation recovery process when the weighted sum of the verification scores exceeds a threshold.

[0008] Furthermore, this application proposes that cascaded encryption includes: generating an optimal data transmission chain by resolving the topological dependencies of measurement points based on a graph neural network; generating a quantum key sequence along the nodes of the transmission chain; performing quantum state polarization encoding at the first node; performing quantum gate controlled non-transformation at intermediate nodes; and restoring the plaintext data at the terminal node by decoding using quantum measurement basis.

[0009] Furthermore, this application also proposes that disabling core operations includes: building a risk situation analysis engine and outputting risk indicators; when the risk level exceeds the security threshold, executing cross-domain modification blocking, electricity bill tracing deactivation, and mobile interface closure; generating a mirror trigger signal based on the duration of the risk; and when the trigger signal is valid, creating an archive snapshot and storing it on a geographically isolated node.

[0010] Furthermore, this application proposes that the driving business flow includes: triggering three analysis flows based on the type characteristics of the file change event, wherein: when the change event involves line parameters, a line loss optimization flow is triggered to generate a loss reduction strategy based on line impedance parameters and load characteristics; when the change event involves electricity pricing policies, an electricity bill tracing flow is triggered to generate anomaly markers based on user load curve reconstruction and historical electricity bill records; when the change event involves equipment topology, a fault prediction flow is triggered to generate an equipment failure probability distribution based on network connectivity indicators; the output results of the analysis flows are encapsulated into blockchain transaction blocks through smart contracts; and the file ledger status is updated after verifying the validity of the transaction based on a consensus mechanism.

[0011] Furthermore, this application proposes that the line loss optimization flow includes: extracting electromagnetic features based on the line material properties and geometric parameters in the change event; converting time-domain load data into frequency-domain feature vectors through Fourier transform; performing electromagnetic transient simulation based on electromagnetic features and frequency-domain vectors to generate a theoretical line loss distribution surface; generating an actual line loss matrix from real-time monitoring data connected to the metering system; calculating the energy efficiency deviation matrix between the theoretical surface and the actual matrix; when the norm of the deviation matrix exceeds a preset tolerance, generating an optimization decision tree based on a reinforcement learning algorithm, the decision tree containing capacitor bank switching commands and transformer tap adjustment strategies; and binding the decision tree with the hash value of the change event to generate a verifiable traceability chain.

[0012] Furthermore, this application proposes that the electricity bill tracing process includes: parsing the archive change log text based on natural language processing technology to extract the electricity price policy feature vector; reconstructing the user's historical load curve through a waveform reconstruction algorithm to construct a high-dimensional mapping model in the feature space; fusing the electricity price vector and load features based on tensor operations to generate a three-dimensional electricity bill calculation tensor; calculating the difference measure between the original electricity bill and the recalculated electricity bill in the tensor space; activating a distributed intelligent tracing work order when the difference measure exceeds the confidence interval threshold, with the work order coordinating multi-node data through a federated learning framework; and writing the tracing results into the spatiotemporal index structure of the archive blockchain, with the index using an R-tree to achieve multi-dimensional range retrieval.

[0013] Furthermore, this application proposes to construct an equipment association network graph based on an archive topology database; calculate the node failure propagation probability using a random walk algorithm to generate an equipment failure probability field; when receiving an equipment status warning signal, perform gradient search in the probability field to locate the coordinates of high-risk equipment; based on electrical connection relationships, generate a risk propagation subgraph using a graph diffusion algorithm with high-risk equipment as the source point; calculate the risk information entropy value based on real-time load spectrum and equipment operating condition data; generate a decision tensor containing maintenance priority, impact range, and loss prediction based on the entropy value; and write the decision tensor into a risk knowledge graph constructed from the graph database.

[0014] Furthermore, this application also proposes the following: constructing a digital twin model of the power system based on real-time archive data, the model including the power grid topology and equipment parameters; injecting disturbance test vectors into the twin model, including short-circuit fault events and load change scenarios; generating a system dynamic response dataset through a parallel computing engine; extracting vulnerability feature vectors from the response data based on topology data analysis methods; mapping entities such as substations and transmission lines as graph nodes, with vulnerability features as node attributes, and constructing a vulnerability knowledge graph; when the node vulnerability index exceeds the safety threshold, generating a hardening strategy based on a graph neural network, the strategy including reactive power compensation point optimization schemes and backup route switching strategies; and pushing strategy instructions to the monitoring and data acquisition system terminal through an industrial communication protocol.

[0015] As can be seen from the above, the intelligent management method and system for electricity marketing archives provided in this application receives archive data through an electricity data acquisition interface and matches it with electricity price feature codes. It constructs an electricity consumption anomaly prediction model to trigger a distributed ledger locking mechanism, generates dynamic default penalty parameters, implements quantum cascade encryption, dynamically associates operation permissions with risk signals, drives business flow in real time, and feeds back to update archives. This solves the problems of low efficiency, weak security protection, and poor business collaboration in traditional archive management. It has the advantages of improving archive management efficiency, enhancing data security protection, and realizing business collaboration. Attached Figure Description

[0016] The above and other objects, features, and advantages of exemplary embodiments of the present invention will become readily apparent from the following detailed description taken in conjunction with the accompanying drawings. Several embodiments of the invention are illustrated in the drawings by way of example and not limitation, wherein:

[0017] Figure 1 This is a flowchart illustrating an intelligent management method for electricity marketing records provided in an embodiment of the present invention. Detailed Implementation

[0018] Preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, which form part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not intended to limit the scope of the present invention.

[0019] like Figure 1 As shown, this application proposes an intelligent management method for electricity marketing records, including the following steps:

[0020] The system receives archive data through a power data acquisition interface, matches the electricity price feature codes in the archives with real-time electricity price policy identifiers, and injects version calibration tags into unmatched data. When performing electricity bill modification operations, it constructs an electricity consumption anomaly prediction model based on the user's historical load patterns and environmental factors, and triggers a distributed ledger locking mechanism when the modified value deviates from the prediction range. When responding to query requests, it generates dynamic default penalty parameters based on user behavior profiles and rule bases. During mobile data transmission, it generates a quantum key distribution path based on the power grid topology and implements cascaded encryption along physical connection nodes. It dynamically associates user operation permissions with power grid operation risk signals, and automatically disables core operations in high-risk states. In a secure and isolated environment, it parses archive data to drive line loss optimization, electricity bill tracing, and fault prediction business flows in real time, and feeds back archive updates based on business outputs.

[0021] The power data acquisition interface refers to the communication port used to receive power marketing archive data. This can be implemented using an API interface or an IoT terminal to achieve real-time acquisition of multi-source heterogeneous data. The real-time electricity price policy identifier is a unique code corresponding to the currently effective electricity price policy. This can be implemented using hash values ​​or digital watermarking technology to quickly match the electricity price feature codes in the archive. The version calibration mark is metadata used to identify data version consistency. This can be implemented using timestamps and Merkle tree structures to ensure the traceability of data that does not match a policy.

[0022] The electricity consumption anomaly prediction model is a machine learning model built based on users' historical load and environmental factors. It can be implemented using LSTM neural networks or random forest algorithms to generate reasonable boundaries for electricity bill modification operations. The distributed ledger locking mechanism is an operation freeze control logic based on blockchain technology. It can be implemented using smart contracts and multi-node consensus mechanisms to prevent abnormal electricity bill modification operations from being executed. The dynamic penalty parameter is a penalty calculation coefficient adjusted in real time based on user behavior profiles. It can be implemented using rule engines and clustering analysis algorithms to ensure that the penalty calculation matches the user's actual behavior. The quantum key distribution path is an encrypted channel generated based on the power grid's physical topology. It can be implemented using a quantum entangled state distribution protocol to ensure the physical layer security of mobile data transmission. Cascaded encryption refers to implementing encryption operations layer by layer at each node of the transmission chain. It can be implemented using quantum polarization coding and quantum gate transformation technology to achieve end-to-end data protection. The power grid operation risk signal is an indicator reflecting the real-time safety status of the power grid. It can be implemented using a risk assessment engine and a situational awareness system to dynamically control user operation permissions. Among them, a secure isolation environment refers to a data processing space that is independent of the production system. This can be achieved using containerization technology or a hardware sandbox to ensure that the business flow processing is not interfered with by external attacks.

[0023] Business output feedback to archive updates refers to writing the line loss optimization results and electricity fee traceability data into the archive database. This can be achieved using a blockchain transaction verification mechanism to form a closed-loop data update chain.

[0024] The power data acquisition interface receives archive data, and real-time electricity price policy identifiers are used to match the electricity price feature codes in the archives. Version calibration flags are injected into unmatched data to ensure data consistency. When performing electricity bill modification operations, an electricity consumption anomaly prediction model is constructed based on the user's historical load patterns and environmental factors. When the modified value deviates from the prediction range, a distributed ledger locking mechanism is triggered to prevent unauthorized modifications. When responding to query requests, user behavior profiles and rule bases are used to generate dynamic penalty parameters. Mobile data transmission adopts a quantum key distribution path based on the power grid topology, implementing cascaded encryption along physical connection nodes to ensure transmission security. User operation permissions are dynamically associated with power grid operation risk signals; core operations are automatically disabled under high-risk conditions. In a secure, isolated environment, archive data parsing drives line loss optimization, electricity bill tracing, and fault prediction business flows; the business output, in turn, updates the archives, forming a closed loop.

[0025] As a preferred embodiment, the specific implementation of this application's solution is as follows: The intelligent management system for electricity marketing archives includes a data acquisition module, an anomaly prediction module, a query response module, an encrypted transmission module, an access control module, and a business-driven module. The data acquisition module connects to the power company's data center via an API interface to receive archive data in real time. The anomaly prediction module uses a long short-term memory neural network to construct an electricity consumption anomaly prediction model, with inputs including user historical electricity consumption, weather data, and other environmental factors. The query response module uses a collaborative filtering algorithm to generate user behavior profiles and calculates dynamic penalty parameters based on a preset rule base. The encrypted transmission module is based on the quantum key distribution protocol BB84 and utilizes photon polarization state encoding information. The access control module adopts a role-based access control model, associating user permissions with a real-time power grid risk level matrix. The business-driven module deploys line loss optimization, electricity bill tracking, and fault prediction algorithms in a Docker container and interacts with the archive system through a message queue. During system operation, when an electricity price policy update signal is received, the data acquisition module automatically scans the archive and updates the electricity price feature code. When performing an electricity bill modification operation, the anomaly prediction module calculates the confidence interval of the modified value; if the value exceeds the interval, a distributed ledger lock is triggered. When transmitting data on a mobile device, the encrypted transmission module performs quantum encryption hop-by-hop along the optimal path. File change events trigger corresponding business flows, and the business output updates the file status after consensus verification.

[0026] Through the above-described scheme, this application achieves real-time and accurate matching between electricity pricing policies and archival data, avoiding errors in electricity bill calculation. Cascaded encryption based on quantum key distribution improves the security of mobile data transmission and resists quantum computing attack threats. Automated correlation between archival change events and business processes enhances the response speed of line loss optimization, electricity bill tracing, and fault prediction. A dynamic penalty parameter generation mechanism enhances the flexibility of electricity bill management. A distributed ledger locking mechanism effectively prevents unauthorized electricity bill modifications. Dynamic correlation between user operation permissions and grid risks improves system security. Overall, this application significantly improves the intelligence level and data security of electricity marketing archival management.

[0027] This application further proposes a specific implementation method for receiving archival data, including triggering the archive scanning engine based on the electricity price policy update signal; identifying the topological coordinates of the metering point through a spatial geocoding converter; mapping the coordinates to the regional electricity price feature matrix; creating a spatiotemporal calibration mark with a version traceability chain using a timestamp generator; and constructing a tamper-resistant storage array using a Merkle tree structure to write the calibration data into the storage array.

[0028] The spatial geocoding converter generates topological coordinates by parsing the physical location information of metering points. The coordinate mapping process matches the metering points with the grid cells of the regional electricity price feature matrix, ensuring that each metering point corresponds to a unique electricity price policy identifier. The timestamp generator generates spatiotemporal calibration markers by overlaying policy update times and operation times. These markers contain version serial numbers and preceding hash values, forming an irreversible traceability chain. The Merkle tree storage array stores the calibration data in a hierarchical structure through hash operations. Each data block contains hash pointers to adjacent nodes, enabling rapid detection of data tampering.

[0029] Specifically, when an electricity price policy update signal triggers the archive scanning engine, the engine traverses all metering point data. The spatial geocoding converter extracts the latitude and longitude information of the metering points and combines it with the power grid topology to generate topological coordinates; for example, mapping a metering point to coordinates (X12, Y35, Z7). These coordinates are input into the regional electricity price feature matrix to match the corresponding electricity price policy code. The timestamp generator concatenates the policy's effective time with the current system time to generate a spatiotemporal calibration marker in the form of "20231001T1530_V2.1.5," and associates the marker with previous versions via a hash chain. The calibrated data is split into multiple data blocks and stored in a Merkle tree structure; for example, the hash value H(A) of data block A is combined with the hash value H(B) of adjacent block B to generate the parent node hash H(AB). When any data block is tampered with, its hash value change will cause an anomaly in the Merkle tree root node hash, triggering a data verification alarm.

[0030] As a preferred embodiment, the solution of this application is specifically implemented as follows:

[0031] The archive scanning engine is triggered by an electricity price policy update signal. This signal is issued by the power company's policy-making department and transmitted to the archive management system via the enterprise service bus. Upon receiving the signal, the archive scanning engine initiates a full-database scan.

[0032] The topological coordinates of metering points are identified using a spatial geocoding converter. The converter uses GPS positioning data and a power grid topology database to convert the physical location of each metering point into standardized latitude and longitude coordinates.

[0033] The coordinates are mapped to a regional electricity price feature matrix. This matrix, pre-built, contains electricity price policy information for different geographical regions. The system uses a spatial indexing algorithm to quickly match the coordinates of metering points to the corresponding matrix cells.

[0034] A timestamp generator is used to create spatiotemporal calibration markers with a version traceability chain. The timestamp generator is based on atomic clock synchronization and is accurate to the millisecond level. Each matching operation generates a unique timestamp, which is associated with coordinate information and electricity price version number to form a traceable version chain.

[0035] A tamper-resistant storage array is constructed using a Merkle tree structure to write calibration data into the array. The Merkle tree employs a binary tree structure, with leaf nodes storing the actual data and non-leaf nodes storing the hash values ​​of their child nodes. After the calibration data is written to a leaf node, the system automatically calculates and updates the root hash value of the tree, ensuring data integrity.

[0036] Through the above technical solutions, this application achieves real-time synchronization of electricity price policy changes and archival data. Spatial geocoding conversion improves the accuracy of meter point location, and the regional electricity price feature matrix accelerates matching efficiency. Spatiotemporal calibration markers ensure data timeliness and traceability, while the Merkle tree structure enhances storage security and tamper resistance. This method significantly improves the automation level and data consistency of archival management, reduces human error, and provides a reliable data foundation for electricity billing calculation and settlement.

[0037] This application further proposes a distributed ledger locking mechanism that includes: constructing a spatiotemporal convolutional neural network model; generating a predicted boundary surface through model inference; activating an operation freeze command when the modified value crosses the surface; generating a distributed ledger evidence package based on the operator's biometrics and the hash of the changed value; performing multi-dimensional collaborative verification, including power grid status verification, user behavior verification, and change trajectory verification; and generating a lock release signal and triggering an operation recovery process when the weighted sum of the verification scores exceeds a threshold.

[0038] The spatiotemporal convolutional neural network model uses a three-dimensional convolutional kernel to process time series and spatial topological data, outputting a predicted boundary surface as the decision benchmark. The predicted boundary surface is generated using a probability density function, and the surface threshold is set as a multiple of the standard deviation of the probability distribution. After the operation freeze command is activated, the operator's biometric features are collected through an iris recognition module, the change value hash is generated using the SHA-256 algorithm, and the evidence package includes a timestamp and device identifier. In the multi-dimensional collaborative verification, power grid status verification calls real-time data from the SCADA system, user behavior verification matches the operation record database, and change trajectory verification compares historical operation sequence patterns. The weighted sum threshold for verification scores is set to 0.85, with a weight allocation ratio of 0.5 for power grid status, 0.3 for user behavior, and 0.2 for change trajectory.

[0039] Specifically, when the electricity bill modification value exceeds the predicted boundary surface, the freeze command immediately interrupts the operation process. Simultaneously, biometric data collection and hash calculation are initiated to generate a certificate containing the operator's identity and data integrity. This certificate is broadcast to preset nodes via a P2P network, triggering grid operation status verification, user operation habit matching, and operation procedure compliance review. During verification, grid load rate and equipment health data are compared in real time with the current operation period; user operation frequency, time period preferences, and historical records are compared for similarity; and the operation step sequence is matched against standard procedures. When the weighted score reaches the release threshold, the blockchain node generates an unlock command containing a timestamp, restoring the electricity bill modification operation permission and writing the verification result to an immutable distributed ledger.

[0040] As a preferred embodiment, the solution of this application is specifically implemented as follows:

[0041] A spatiotemporal convolutional neural network model was constructed. This model employs a three-dimensional convolutional kernel, and the input layer contains user historical load data, ambient temperature data, and temporal feature data. The hidden layers consist of four convolutional layers and two fully connected layers, with the ReLU activation function chosen.

[0042] The predicted boundary surface is generated through model inference. Using the next 24 hours as the prediction time window, one prediction point is generated every hour to form a 3D predicted surface. The upper and lower boundaries of the surface are determined using the confidence interval method, with a confidence level set to 95%.

[0043] The operation freeze command is activated when the modified value crosses the surface. The system compares the positional relationship between the modified value and the predicted surface in real time, and immediately sends a freeze command to the operation terminal once the crossing behavior is detected.

[0044] A distributed ledger evidence package is generated based on the operator's biometric features and the hash of the modified value. The operator's fingerprint and iris features are collected, combined with the SHA256 hash of the modified value, and packaged to generate evidence data.

[0045] Multi-dimensional collaborative verification is performed, including power grid status verification, user behavior verification, and change trajectory verification. Power grid status verification is based on real-time SCADA data; user behavior verification uses anomaly detection algorithms; and change trajectory verification is achieved through blockchain traceability.

[0046] When the weighted sum of verification scores exceeds the threshold, a lock release signal is generated and the operation recovery process is triggered. The verification score threshold is set to 0.8. When the weighted sum exceeds this value, the system automatically generates an unlock command to restore operation privileges.

[0047] Through the above technical solution, this application achieves intelligent management and control of electricity bill modification operations. A predictive model built based on user electricity consumption patterns and environmental factors can accurately identify abnormal modification behavior. A multi-dimensional collaborative verification mechanism further enhances operational security. The application of distributed ledger technology ensures the immutability and traceability of modification records. This solution effectively prevents human error and malicious tampering, improving the accuracy and reliability of electricity bill management.

[0048] This application further proposes a method to generate an optimal data transmission chain based on the topological dependency of metric points using a graph neural network, generate a quantum key sequence along the nodes of the transmission chain, perform quantum state polarization encoding at the first node, perform quantum gate controlled non-transformation at the intermediate nodes, and restore the plaintext data by decoding using quantum measurement basis at the terminal node.

[0049] The graph neural network generates a topology dependency graph by analyzing the physical connections and electrical parameters between measurement points, and generates a data transmission chain with minimum transmission delay based on path length and node load. The quantum key sequence generates a polarization state sequence of corresponding length according to the number of nodes in the transmission chain, with each node assigned a unique polarization angle. The first node uses the BB84 protocol to polarize the quantum state, with the polarization angle consistent with a preset value in the key sequence. Intermediate nodes perform phase-flipping operations on the quantum state using controlled NOT gates, with the phase-flipping parameters dynamically generated from the key sequence values ​​of the predecessor nodes. The terminal node performs quantum state projection measurements according to a pre-shared measurement basis selection rule, with the measurement basis direction orthogonal to the last value of the key sequence.

[0050] Specifically, when a mobile device initiates a data transmission request, the connection relationships of metering points in the power grid topology are input into a graph neural network to generate a data transmission chain containing three intermediate nodes. The first node generates a random sequence of polarization angles of 0°, 45°, 90°, and 135° to polarize the photons of the transmitted data. The intermediate nodes generate phase flipping parameters based on the polarization angle values ​​of the predecessor nodes; for example, when a 45° polarization angle is detected in the predecessor node, a π / 2 phase rotation is applied. The terminal node selects the measurement basis according to preset rules: if the last bit of the key sequence is 0, a horizontal / vertical basis is used; if the last bit is 1, a diagonal basis is used for measurement. Through the polarization encoding and phase transformation of the quantum state, eavesdroppers cannot simultaneously obtain polarization and phase information. The distribution path of the quantum key is strictly bound to the physical topology of the power grid, effectively resisting man-in-the-middle attacks.

[0051] As a preferred embodiment, the solution of this application is specifically implemented as follows:

[0052] This paper utilizes a graph neural network (Graph Neural Network) to analyze the topological dependencies of metering points and generate the optimal data transmission chain. The Graph Neural Network employs a message-passing mechanism, treating metering points as graph nodes and power lines as edges. By iteratively updating the node feature vectors, an embedded representation reflecting the topological structure is obtained. Based on this representation, a shortest path algorithm is applied to determine the optimal path for data transmission.

[0053] A quantum key sequence is generated along the transmission chain nodes. A quantum key distribution device is deployed at each node, and a shared key is established between adjacent nodes using the BB84 protocol. The key length is set to 256 bits, and the refresh period is 10 minutes.

[0054] Quantum state polarization encoding is performed at the first node. The classical bit string to be transmitted is mapped to a sequence of quantum bits, and the information is encoded using four polarization states {|H>,|V>,|+>,|->}. Here, |H> and |V> represent horizontal and vertical polarization, respectively, and |+> and |-> represent diagonal polarization.

[0055] A quantum gate-controlled non-transformation is performed at the intermediate node. A CNOT gate operation is applied to the received quantum state, using the quantum key bit as the control bit and the transmitted data bit as the target bit. This process achieves quantum entanglement, enhancing security.

[0056] At the terminal node, plaintext data is decoded and restored using a quantum measurement basis. The receiver measures the quantum state according to a pre-agreed measurement basis with the sender, and XORs the measurement result with the key to restore the original data.

[0057] Through the above technical solutions, this application achieves end-to-end secure data transmission based on quantum key distribution and quantum state manipulation. Utilizing the quantum no-cloning theorem and the principle of quantum superposition, it effectively prevents the computational complexity security threats faced by traditional cryptography. Simultaneously, by implementing encryption at the physical layer, the process of decryption and re-encryption at intermediate nodes is avoided, reducing the risk of information leakage. Furthermore, the transmission path optimized based on the power grid topology improves communication efficiency and reduces quantum channel loss.

[0058] This application further proposes to construct a risk situation analysis engine to output risk indicators. When the risk level exceeds the safety threshold, cross-domain modification blocking, electricity bill tracing shutdown and mobile interface closure are executed. A mirror trigger signal is generated based on the duration of the risk. When the trigger signal is valid, a file snapshot is created and stored in a geographically isolated node.

[0059] The risk situation analysis engine generates dynamic risk scores by collecting real-time power grid operating parameters and user operation logs, using a multi-dimensional risk assessment algorithm. Cross-domain modification blocking cuts off the core database write channel via a modification command interceptor; electricity billing traceability is disabled by shutting down the billing system data interface; and mobile interface closure is achieved by cutting off the power supply circuit to the wireless communication module. The mirror trigger signal generation module has a built-in time threshold comparator that outputs a high-level signal when the risk duration exceeds a preset threshold. Geographically isolated nodes use offline storage media and are physically disconnected from the main network.

[0060] Specifically, when the risk situation analysis engine detects power grid overload or abnormal login behavior, it calculates a risk score in real time. When the score exceeds a preset security threshold, the modified command interceptor cuts off the TCP / IP communication port of the core database, the billing system data interface closes the process to terminate the SQL query service, and the power supply circuit of the wireless communication module is disconnected via relay contacts. If the risk state persists for more than 300 seconds, the time threshold comparator triggers the mirror signal generation circuit, starts the snapshot generation process to compress the current archive data into a read-only format, and writes it to the offline storage device via a USB isolator. The offline storage device is deployed in a separate data center, maintaining a physical distance of more than 20 meters from the main network to prevent network attack penetration.

[0061] As a preferred embodiment, the solution of this application is specifically implemented as follows:

[0062] A risk situation analysis engine is constructed to output risk indicators. Based on multi-dimensional data such as power grid operating parameters, load fluctuations, and weather conditions, the risk situation analysis engine builds a neural network model to calculate the system risk index in real time. The risk index uses a quantitative standard of 0-100, with 0-30 indicating low risk, 31-70 indicating medium risk, and 71-100 indicating high risk.

[0063] When the risk level exceeds the security threshold, cross-domain modification blocking, electricity bill tracing disabling, and mobile interface shutdown are implemented. The security threshold is set to 70, meaning the protection mechanism is triggered when the risk index reaches 71. Cross-domain modification blocking restricts cross-network segment data modification operations through firewall rules. Electricity bill tracing disabling blocks the historical electricity bill query interface through database views. Mobile interface shutdown temporarily disables mobile device access through the API gateway.

[0064] A mirror trigger signal is generated based on the duration of the risk. A trigger signal is generated when the risk duration exceeds 30 minutes. The trigger signal is signed using the RSA encryption algorithm to ensure its authenticity.

[0065] When the trigger signal is valid, a snapshot of the archive is created and stored on a geographically isolated node. The archive snapshot uses incremental backup, only synchronizing data changed after the risk occurs. The geographically isolated node is located in a remote data center more than 200 kilometers away from the main data center, and data is transmitted via a dedicated network line.

[0066] Through the above technical solutions, this application achieves dynamic risk prevention and control for the power marketing file management system. Through real-time risk situation analysis, the system can promptly identify potential threats and automatically trigger multi-layered protection measures. Mechanisms such as cross-domain modification blocking, electricity bill tracing suspension, and mobile interface closure effectively prevent illegal operations and data leaks under high-risk conditions. A mirror triggering mechanism based on risk duration ensures off-site backup of critical data, improving the system's disaster recovery capabilities. This dynamic, multi-dimensional security protection strategy significantly enhances the security and reliability of power marketing file management, providing strong support for the stable operation of power companies.

[0067] This application further proposes a method for driving business flows, including triggering a three-way analysis flow based on the type characteristics of file change events; triggering a line loss optimization flow when the change event involves line parameters, generating a loss reduction strategy based on line impedance parameters and load characteristics; triggering an electricity bill tracing flow when the change event involves electricity pricing policies, generating anomaly markers based on user load curve reconstruction and historical electricity bill records; triggering a fault prediction flow when the change event involves equipment topology, generating an equipment failure probability distribution based on network connectivity indicators; encapsulating the output results of the analysis flows into blockchain transaction blocks through smart contracts; and updating the file ledger status after verifying the validity of the transactions based on a consensus mechanism.

[0068] The triggering conditions of the three analysis streams strictly correspond to the types of change events. For example, a change in line parameters activates the line loss optimization stream, which converts time-domain load data into frequency-domain feature vectors using Fourier transform and performs transient simulations based on electromagnetic features to generate a theoretical line loss distribution surface. The electricity bill tracing stream uses natural language processing to parse change log text, reconstructs the user's historical load curve using waveform reconstruction algorithms, and calculates the difference between the original and recalculated electricity bills in tensor space. The fault prediction stream calculates the probability of node failure propagation based on the device association network graph, generates a risk propagation subgraph, and calculates the information entropy value. The smart contract encapsulates the output results of each analysis stream into blockchain transaction blocks. The validity of the transaction needs to be verified by the consensus mechanism, and the archive ledger status is updated after verification.

[0069] Specifically, when line parameters change, the system extracts the electromagnetic features corresponding to the line material properties and geometric parameters, combines them with frequency domain feature vectors to perform electromagnetic transient simulation, and generates a theoretical line loss distribution surface. By comparing the actual line loss matrix monitored in real time, the norm of the energy efficiency deviation matrix is ​​calculated. When the deviation exceeds the preset tolerance, a reinforcement learning algorithm generates an optimized decision tree containing capacitor bank switching commands and transformer tap adjustment strategies. When electricity pricing policies change, the system parses log text to extract electricity price feature vectors, reconstructs the user's historical load curve, and fuses the electricity price vector and load features in tensor space to calculate the difference metric. If the difference exceeds the confidence interval threshold, a distributed intelligent traceability work order is activated, and traceability is completed through a federated learning framework that coordinates data from multiple nodes. When equipment topology changes, the system constructs an equipment association network graph, calculates the node failure probability through a random walk algorithm, and after receiving a warning signal, performs gradient search to locate high-risk equipment, generates a risk propagation subgraph, and calculates the information entropy value. The output results of each analysis stream are encapsulated into blockchain transaction blocks by smart contracts, and the archive ledger is updated after verification through a consensus mechanism to achieve cross-system data synchronization.

[0070] As a preferred embodiment, a three-way analysis stream is triggered based on the type characteristics of file change events. When a change event involves line parameters, a line loss optimization stream is triggered, generating a loss reduction strategy based on line impedance parameters and load characteristics. Specifically, firstly, the line material properties and geometric parameters in the change event are extracted, and electrical parameters such as resistance and inductance are calculated. Then, real-time current and voltage data from the substation outgoing lines are collected, and the load spectral characteristics are obtained through Fast Fourier Transform. Further, the electrical parameters and spectral characteristics are input into power system transient simulation software to simulate the theoretical loss distribution of each line. Simultaneously, actual line loss data is obtained from the distribution automation system and compared with theoretical values. When the actual line loss deviates from the theoretical value by more than 5%, an optimization algorithm is initiated to generate an adjustment scheme, such as adjusting the switching time of reactive power compensation devices or optimizing the distribution network topology.

[0071] When a change event involves electricity pricing policy, an electricity billing retrospective process is triggered, generating anomaly markers based on user load curve reconstruction and historical electricity bill records. Specifically, firstly, natural language processing technology is used to extract key information about the old and new electricity pricing policies, such as time-of-use pricing and tiered pricing, from the archive change log. Then, historical load data of users is obtained from the electricity consumption information collection system, and signal processing methods such as wavelet transform are used to reconstruct the complete load curve. Further, the electricity price information and load curve are input into the electricity billing calculation model to obtain the recalculated electricity bill value. This value is compared with the original electricity bill record, and when the difference exceeds 1%, a retrospective work order is automatically generated. The retrospective results are synchronized across multiple business systems through distributed ledger technology.

[0072] When a change event involves equipment topology, a fault prediction flow is triggered, generating an equipment failure probability distribution based on network connectivity metrics. In practice, the equipment topology relationships are first extracted from the asset management system to construct a power grid graph model. Then, based on historical fault data, the Monte Carlo method is used to simulate the failure probabilities of various types of equipment. Furthermore, graph theory algorithms are used to calculate metrics such as degree centrality and betweenness centrality of network nodes to assess the importance of equipment within the overall system. Finally, a risk warning list is generated by combining the equipment failure probabilities and importance.

[0073] The analysis stream output is encapsulated into blockchain transaction blocks using smart contracts. Specifically, information such as line loss optimization schemes, electricity cost tracking results, and equipment risk warnings are organized into transaction content according to a predefined data structure. The transaction is then digitally signed using a private key and broadcast to the blockchain network. Other nodes in the network verify the transaction's legitimacy and package it into a new block.

[0074] After verifying the validity of a transaction based on a consensus mechanism, the ledger status is updated. In practice, a Proof-of-Stake (PoS) consensus algorithm is used, where pre-selected verification nodes vote on new blocks. When more than two-thirds of the nodes reach a consensus, the new block is confirmed as valid. Subsequently, each node appends the new block to its local ledger and updates the corresponding file status. For example, optimized line parameters are written to the device file, and retrospective electricity bill data is updated to the user file.

[0075] This application further proposes to extract electromagnetic features based on the line material properties and geometric parameters in the change event; convert the time-domain load data into a frequency-domain feature vector through Fourier transform; perform electromagnetic transient simulation based on the electromagnetic features and frequency-domain vector to generate a theoretical line loss distribution surface; generate an actual line loss matrix from the real-time monitoring data connected to the metering system; calculate the energy efficiency deviation matrix between the theoretical surface and the actual matrix; when the norm of the deviation matrix exceeds a preset tolerance, generate an optimized decision tree based on a reinforcement learning algorithm, the decision tree containing capacitor bank switching commands and transformer tap adjustment strategies; and bind the decision tree with the hash value of the change event to generate a verifiable traceability chain.

[0076] The electromagnetic feature vector is constructed using resistivity and permeability parameters for the line material properties, and the spatial electromagnetic field distribution is calculated using conductor cross-sectional area and length data for geometric parameters. Fourier transform uses a fast algorithm to decompose the time-domain current waveform into fundamental and harmonic components, forming frequency-domain amplitude and phase feature vectors. Electromagnetic transient simulation is based on solving Maxwell's equations using the finite element method to generate the theoretical power loss distribution for each section of the line. The actual line loss matrix calculates active power by collecting instantaneous voltage and current values ​​from metering terminals and generates spatial distribution data statistically according to time windows. The energy efficiency deviation matrix constructs a two-dimensional difference mapping by calculating the Euclidean distance between theoretical and actual values ​​point by point. The reinforcement learning algorithm uses a Q-learning model, with the deviation matrix as the state space and capacitor bank switching and tap changer adjustment as the action space, iteratively generating the optimal decision tree through a reward function. Hash value binding uses the SHA-256 algorithm to encrypt and bind decision tree operation instructions with unique identifiers of change events.

[0077] As a preferred embodiment, the solution of this application is specifically implemented as follows:

[0078] In the line loss optimization process, electromagnetic features are first extracted based on the line material properties and geometric parameters in the change event. For example, for aluminum conductors, parameters such as resistivity and permeability are extracted; for geometric parameters, data such as conductor cross-sectional area and line length are extracted.

[0079] Furthermore, the time-domain load data is converted into a frequency-domain feature vector using Fourier transform. Specifically, a fast Fourier transform is performed on the 24-hour load curve to obtain the spectral characteristics containing the fundamental and harmonic components.

[0080] Therefore, electromagnetic transient simulation is performed based on the extracted electromagnetic features and frequency domain vectors. The simulation uses EMTP-RV software to construct a circuit model including power supply, transformer, line, and load, inputs frequency domain load features, and calculates the theoretical line loss distribution surface.

[0081] Next, an actual line loss matrix is ​​generated from the real-time monitoring data connected to the metering system. Data on the outgoing power of the substation and the incoming power of the distribution transformer are collected through the SCADA system, and the actual line loss value is calculated to form a three-dimensional matrix.

[0082] Furthermore, the energy efficiency deviation matrix between the theoretical surface and the actual lattice is calculated. The actual line loss surface is obtained by fitting the actual lattice using the least squares method, and the deviation matrix is ​​obtained by subtracting it from the theoretical surface.

[0083] When the norm of the deviation matrix exceeds a preset tolerance, an optimized decision tree is generated based on a reinforcement learning algorithm. Specifically, the Q-learning algorithm is used, with the reduction in line loss as the reward function, to learn the optimal capacitor bank switching strategy and transformer tap adjustment scheme.

[0084] Finally, the decision tree is bound to the hash value of the change event to generate a verifiable traceability chain. The SHA-256 algorithm is used to hash the decision tree and the change event ID, and the hash value is written to the blockchain to achieve decision traceability.

[0085] This application further proposes a method to parse archive change log text using natural language processing technology to extract electricity price policy feature vectors; to reconstruct user historical load curves using waveform reconstruction algorithms and construct a high-dimensional mapping model in the feature space; to generate a three-dimensional electricity charge calculation tensor by fusing electricity price vectors and load features based on tensor operations; to calculate the difference measure between the original electricity charge and the recalculated electricity charge in the tensor space; to activate a distributed intelligent traceability work order when the difference measure exceeds the confidence interval threshold, with the work order coordinating multi-node data through a federated learning framework; and to write the traceability results into the spatiotemporal index structure of the archive blockchain, with the index using an R-tree to achieve multi-dimensional range retrieval.

[0086] Among these technologies, natural language processing (NLP) identifies policy keywords and numerical parameters in change logs through a semantic parsing model, generating structured feature vectors; waveform reconstruction algorithms, based on time series interpolation and filtering, restore the complete shape of user load curves and map them to a high-dimensional feature space to eliminate noise interference; tensor operations superimpose the electricity price vector and load features according to time, space, and policy dimensions to construct a three-dimensional electricity bill calculation model; the difference metric is calculated using Euclidean distance in tensor space and combined with confidence interval thresholds to identify outliers; the distributed intelligent traceability work order adopts a federated learning framework to aggregate historical electricity bill records and load data from multiple nodes while protecting data privacy; and the spatiotemporal index structure uses R-trees to spatially partition and temporally slice the traceability results, supporting multi-dimensional range queries.

[0087] As a preferred embodiment, the specific implementation of this application is as follows: During the execution of the electricity bill tracing process, the BERT model is first used to perform semantic parsing on the text data in the file change log, extracting the electricity price policy adjustment range, effective time range, and applicable area code to form a feature vector. Waveform reconstruction of the user's historical electricity consumption time series is performed using inverse Fourier transform, constructing a high-dimensional mapping model in the feature space that includes load fluctuation patterns and electricity consumption habits. Tensor decomposition technology is used to perform multimodal fusion of the electricity price feature vector and load curve features, generating a three-dimensional electricity bill calculation tensor containing the electricity price coefficient, load weight, and time dimension. The KL divergence algorithm is applied in the tensor space to calculate the difference measure between the original electricity bill and the recalculated electricity bill. When the difference value exceeds the upper limit of the confidence interval, an intelligent tracing work order based on the Hyperledger Fabric framework is activated. This work order uses a federated learning mechanism to collaboratively schedule distributed data nodes of the metering system, marketing system, and financial system, and finally writes the tracing results into the blockchain database in JSON format. The spatiotemporal index uses an R-tree structure to achieve multi-dimensional range retrieval by time interval, geographical region, and user category.

[0088] This application further proposes a method to parse archive change log text using natural language processing technology to extract electricity price policy feature vectors; to reconstruct user historical load curves using waveform reconstruction algorithms and construct a high-dimensional mapping model in the feature space; to generate a three-dimensional electricity charge calculation tensor by fusing electricity price vectors and load features based on tensor operations; to calculate the difference measure between the original electricity charge and the recalculated electricity charge in the tensor space; to activate a distributed intelligent traceability work order when the difference measure exceeds the confidence interval threshold, with the work order coordinating multi-node data through a federated learning framework; and to write the traceability results into the spatiotemporal index structure of the archive blockchain, with the index using an R-tree to achieve multi-dimensional range retrieval.

[0089] Among them, the natural language processing technology adopts a log text parsing model based on the attention mechanism to extract the effective time, billing rules and applicable area feature vectors in the electricity pricing policy, with the feature dimension set to 128 dimensions; the waveform reconstruction algorithm fills in the missing load data points through interpolation and generates continuous load waveforms by fitting cubic spline curves, with the sampling frequency set to once per minute; the three-dimensional electricity charge calculation tensor consists of the electricity charge feature vector, load curve features and time dimension, with a tensor dimension of 128×1440×30; the difference metric calculates the electricity charge difference for each time segment through tensor slicing operation, with the difference threshold set to ±5%; the distributed intelligent traceability work order adopts a federated learning framework based on differential privacy, and coordinates at least three regional nodes to verify the data; the spatiotemporal index structure divides the traceability results into spatial regions according to timestamps and geographical coordinates through R-trees, with each regional index node storing 256 data blocks.

[0090] Specifically, the file change log text is converted into a vector sequence through the word embedding layer in the natural language processing model. Key features of the electricity pricing policy are extracted via a bidirectional long short-term memory network, outputting a 128-dimensional feature vector. Historical load data is normalized using a waveform reconstruction algorithm, and discrete data points are converted into frequency domain signals using a fast Fourier transform to reconstruct a complete daily load curve. The electricity price feature vector and load feature vector are used to generate a three-dimensional electricity cost calculation tensor through tensor cross product operations, with each tensor unit storing hourly electricity cost calculation parameters. When calculating the difference metric, the tensor is sliced ​​along the time dimension, and the Euclidean distance formula is used to calculate the deviation between the original and recalculated electricity cost vectors. When the deviation exceeds a threshold for three consecutive time slices, the traceability work order distributes gradient update instructions to regional nodes through the parameter server in the federated learning framework. After local model training, the nodes upload encrypted gradient parameters. The traceability result data packet is hashed using SHA-256, and time windows are divided according to timestamps. Data in each window is stored in the leaf nodes of an R-tree index according to geographical coordinate range, with the index branch factor set to 8, supporting millisecond-level spatiotemporal range queries.

[0091] As a preferred embodiment, the specific implementation of this application is as follows: A network graph of equipment associations is constructed based on the connection relationships of circuit breakers and transformers in the power equipment topology database, where network nodes represent equipment entities and edges represent electrical connections. A random walk algorithm is used to simulate equipment failure propagation paths, and probability transfer operations are performed between equipment nodes to generate a probability field matrix containing failure propagation weights. When an overload warning signal is received from a relay protection device, a gradient descent search algorithm is executed in the probability field to locate the coordinates of high-risk equipment with a probability gradient change rate exceeding 0.8. Using this equipment as the source point, a risk propagation subgraph containing three levels of associated equipment is generated using a graph diffusion algorithm, where the edge weights of the subgraph reflect the fault propagation intensity. Based on real-time collected load fluctuation spectrum data and equipment temperature and operating condition data, a risk information entropy value of 2.3 bits is calculated. A decision tensor containing "prioritize maintenance of high-voltage side circuit breakers" is generated based on the entropy value. This tensor contains data on the equipment maintenance sequence and the range of impact covering 12 distribution units. Finally, the decision data is written into a risk knowledge graph constructed from the graph database, forming a three-dimensional visualization graph containing equipment entities, risk paths, and handling strategies.

[0092] This application further proposes constructing a digital twin model of the power system based on real-time archive data. The model includes the power grid topology and equipment parameters. Disturbance test vectors, including short-circuit fault events and load change scenarios, are injected into the twin model. A system dynamic response dataset is generated through a parallel computing engine. Vulnerability feature vectors are extracted from the response data based on topology data analysis methods. Entities such as substations and transmission lines are mapped as graph nodes, and vulnerability features are node attributes, constructing a vulnerability knowledge graph. When the node vulnerability index exceeds the safety threshold, a hardening strategy is generated based on a graph neural network. The strategy includes reactive power compensation point optimization schemes and backup route switching strategies. The strategy instructions are pushed to the monitoring and data acquisition system terminal through an industrial communication protocol.

[0093] In constructing the digital twin model of the power system, the power grid topology in the real-time archive data is dynamically loaded through a graph database, and the equipment parameters include transformer capacity and line impedance values. The disturbance test vector is generated using the Monte Carlo method, and the short-circuit fault events include simulated parameters for three-phase short circuits and single-phase ground faults. The step amplitude of the load change scenario is set to 120%-150% of the rated load. The parallel computing engine adopts a GPU-accelerated architecture, and the dynamic response dataset includes voltage sag waveforms and frequency offsets. The topology data analysis method uses the continuous coherence algorithm, and the vulnerability feature vector extracted from the response data includes node betweenness centrality and edge weight fluctuation coefficients. The node attributes of the vulnerability knowledge graph include equipment aging coefficients and load rate indicators. The graph neural network adopts a graph attention mechanism, and the generated reactive power compensation point optimization scheme includes capacitor bank switching coordinates and capacity parameters. The backup route switching strategy includes priority ranking of at least two redundant paths. The industrial communication protocol adopts the IEC 61850 standard, and the policy instructions are encapsulated and transmitted through the manufacturing message specification format.

[0094] Specifically, the power grid topology in real-time archive data is dynamically loaded into the digital twin model via a graph database, and transformer capacity data in the equipment parameters drives the model initialization process. When a short-circuit fault event is injected, the three-phase short-circuit parameters are set to a transition resistance of 0.1Ω, a fault duration of 100ms, and a step amplitude of 130% of the rated load for load surge scenarios. The parallel computing engine calls CUDA kernel functions to solve differential-algebraic equations in parallel on a GPU cluster, generating dynamic response data containing a voltage sag of 0.7 pu. The continuous cohomology algorithm performs topology persistence analysis on the response data, extracting vulnerability features where node betweenness centrality exceeds 0.85. In the knowledge graph, a warning mechanism is triggered when the load factor of a transmission line node reaches 90%. The graph attention network performs weighted aggregation of node vulnerability indicators, and the generated reactive power compensation scheme specifies the deployment of an 8Mvar capacitor bank at the topology coordinates (X=235, Y=418). The backup route switching strategy calculates the shortest path using Dijkstra's algorithm, with priority ranking based on line current carrying capacity margin. The final generated policy instructions are encapsulated into MMS messages using the IEC 61850 protocol and transmitted to the substation RTU terminal for execution via fiber optic channel.

[0095] As a preferred embodiment, the specific implementation of this application is as follows: A digital twin model of the power grid, including a 220kV substation, 10kV distribution lines, and user transformers, is constructed. The power grid topology is imported using the CIM standard format, and equipment parameters are standardized according to the IEC 61850 protocol. Three-phase short-circuit fault test vectors are injected into the twin model, with the fault location set at 25% of the distribution line length, while simulating a test scenario of a 30% load surge. Parallel electromagnetic transient calculations are performed using a GPU cluster to generate a response dataset containing node voltage drop depth and protection action timing. A topology persistent coherence algorithm is used to extract equipment overload probability and voltage stability coefficients from the response data as vulnerability features, mapping the transmission lines to graph nodes and assigning vulnerability weights. When the vulnerability index of a certain 110kV line node reaches 0.87, a reinforcement strategy including the installation coordinates of a newly added SVG reactive power compensation device is generated using a graph attention network, while simultaneously planning a backup line switching path. This strategy is encapsulated as control commands using the IEC 60870-5-104 protocol and pushed to the SCADA system of the target substation for execution via an encrypted channel.

[0096] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for intelligent management of electricity marketing records, characterized in that, include: The system receives archive data through the power data acquisition interface, matches the electricity price feature code in the archive based on the real-time electricity price policy identifier, and injects a version calibration mark into the unmatched data. When an electricity bill modification operation is performed, an electricity consumption anomaly prediction model is constructed based on the user's historical load pattern and environmental factors. When the modified value deviates from the prediction range, a distributed ledger locking mechanism is triggered. When responding to a query request, dynamic penalty parameters are generated based on user behavior profiles and the rule base; During mobile data transmission, a quantum key distribution path is generated based on the power grid topology, and cascaded encryption is implemented along the physical connection nodes. Dynamically link user operation permissions to power grid operation risk signals, and automatically disable core operations in high-risk states; In a secure, isolated environment, the system analyzes archive data to drive real-time business processes such as line loss optimization, electricity cost tracking, and fault prediction, and uses business outputs to feed back into archive updates.

2. The method according to claim 1, characterized in that, The received archive data includes: The archive scanning engine is triggered by the electricity price policy update signal; Identify the topological coordinates of measurement points using a spatial geocoding converter; Map the coordinates to the regional electricity price feature matrix; Create spatiotemporal calibration markers with version traceability chains using a timestamp generator; A Merkle tree structure is used to construct a tamper-resistant storage array, and calibration data is written into the storage array.

3. The method according to claim 1, characterized in that, The distributed ledger locking mechanism includes: Construct a spatiotemporal convolutional neural network model; Predicted boundary surfaces are generated through model inference; The operation freeze command is activated when the modified value crosses a surface; A distributed ledger evidence package is generated based on the operator's biometric features and change value hashes. Perform multi-dimensional collaborative verification, including power grid status verification, user behavior verification, and change trajectory verification; When the weighted sum of the verification scores exceeds the threshold, a lock release signal is generated and the operation recovery process is triggered.

4. The method according to claim 1, characterized in that, The concatenated encryption includes: The optimal data transmission chain is generated by analyzing the topological dependencies of measurement points using a graph neural network. Generate quantum key sequences along the transmission chain nodes; Quantum state polarization encoding is performed at the first node; Perform quantum gate-controlled non-transformation at intermediate nodes; The plaintext data is decoded and restored at the terminal node using a quantum measurement base.

5. The method according to claim 1, characterized in that, The disabled core operations include: Build a risk situation analysis engine and output risk indicators; When the risk level exceeds the safety threshold, cross-domain modification is blocked, electricity bill tracing is disabled, and mobile interface is closed. Generate a mirror trigger signal based on the duration of the risk; When the trigger signal is valid, a file snapshot is created and stored on a geographically isolated node.

6. The method according to claim 1, characterized in that, The driving service flow includes: A three-way analysis flow is triggered based on the type characteristics of the file change event, where: When a change event involves line parameters, a line loss optimization flow is triggered to generate a loss reduction strategy based on line impedance parameters and load characteristics. When a change event involves electricity pricing policy, an electricity bill tracing flow is triggered, generating anomaly markers based on user load curve reconstruction and historical electricity bill records. When a change event involves a device topology, a fault prediction flow is triggered, which generates a device failure probability distribution based on network connectivity metrics. The analysis stream output is encapsulated into blockchain transaction blocks using smart contracts; The ledger status is updated after the validity of the transaction is verified based on the consensus mechanism.

7. The method according to claim 1, characterized in that, The line loss optimization flow includes: Electromagnetic features are extracted based on the material properties and geometric parameters of the line in the change event; The time-domain load data is converted into a frequency-domain feature vector using Fourier transform; Electromagnetic transient simulation is performed based on electromagnetic characteristics and frequency domain vectors to generate a theoretical line loss distribution surface; Generate an actual line loss matrix from the real-time monitoring data connected to the metering system; Calculate the energy efficiency deviation matrix between the theoretical surface and the actual lattice; When the norm of the deviation matrix exceeds the preset tolerance, an optimized decision tree is generated based on the reinforcement learning algorithm. The decision tree includes capacitor bank switching commands and transformer tap adjustment strategies. Bind the decision tree to the hash value of the change event to generate a verifiable traceability chain.

8. The method according to claim 6, characterized in that, The electricity bill tracing process includes: Natural language processing technology was used to parse the archive change log text and extract the feature vector of electricity pricing policy. The user's historical load curve is reconstructed using a waveform reconstruction algorithm, and a high-dimensional mapping model is constructed in the feature space. Based on tensor operations, a three-dimensional electricity cost calculation tensor is generated by fusing electricity price vectors and load characteristics. Calculate a measure of the difference between the original electricity charge and the recalculated electricity charge in tensor space; When the difference metric exceeds the confidence interval threshold, the distributed intelligent traceability work order is activated. The work order coordinates data from multiple nodes through a federated learning framework. The tracing results are written into the spatiotemporal index structure of the archive blockchain, and the index uses an R-tree to achieve multi-dimensional range retrieval.

9. The method according to claim 6, characterized in that, Construct a device association network diagram based on the archive topology database; The probability of node failure propagation is calculated using a random walk algorithm to generate a device failure probability field; when a device status warning signal is received, gradient search is performed in the probability field to locate the coordinates of high-risk devices. Based on electrical connection relationships, a risk propagation subgraph is generated using a graph diffusion algorithm, with high-risk equipment as the source point. Calculate the risk information entropy value based on real-time load spectrum and equipment operating condition data; Generate a decision tensor containing maintenance priority, impact range, and loss estimate based on the entropy value; The decision tensor is written into a risk knowledge graph constructed from a graph database.

10. The method according to claim 1, characterized in that, Also includes: A digital twin model of the power system is built based on real-time archive data. The model includes the power grid topology and equipment parameters. Inject disturbance test vectors into the twin model, including short-circuit fault events and load change scenarios; Generate a dynamic system response dataset using a parallel computing engine; Vulnerability feature vectors are extracted from response data based on topological data analysis methods; Entities such as substations and transmission lines are mapped as graph nodes, and vulnerability features are node attributes to construct a vulnerability knowledge graph; When the node vulnerability index exceeds the security threshold, a reinforcement strategy is generated based on the graph neural network. The strategy includes a reactive power compensation point optimization scheme and a backup route switching strategy. Policy commands are pushed to the monitoring and data acquisition system terminal via industrial communication protocols.

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