Message analysis and verification method in automatic joint debugging of power distribution terminal

Abstract

The invention relates to the technical field of power distribution terminals, and discloses a power distribution terminal automatic joint debugging message analysis and verification method, which comprises the following steps: constructing an intelligent message analysis system, carrying out feature extraction and semantic understanding on a power distribution terminal message by adopting a multi-modal deep learning network, and generating structured feature representation; a heterogeneous edge computing framework is deployed, a special processing unit is integrated on a power distribution terminal side, message localization analysis and feature compression are achieved, and the network transmission efficiency is optimized; establishing a decentralized verification network, and ensuring verifiability and tamper-proofing performance of a message verification process through a distributed consensus mechanism; and designing an adaptive verification strategy engine, and dynamically optimizing verification rules and parameter configuration based on environmental perception and historical data. The message analysis and verification method in the automatic joint debugging of the power distribution terminal aims at achieving intelligent analysis, safety verification and efficient processing of the message of the power internet of things and providing reliable data safety guarantee and intelligent decision support for a power system.

Description

Technical Field

[0001] This invention relates to the field of power distribution terminal technology, specifically to a method for message parsing and verification during automatic commissioning of power distribution terminals. Background Technology

[0002] Message parsing and verification technology in automatic commissioning of distribution terminals is a key link in the construction of smart distribution networks. Currently, the main technical solutions in this field include centralized message processing architecture and rule-based traditional verification methods. For example, Chinese patent CN117335569A discloses a distribution network point-to-point commissioning information interaction method, which establishes a secure channel with the master station through a smart chip encrypted wireless communication module, downloads the debugging task sheet using file service, and exchanges test information in 104 message format. Another Chinese patent CN110618328A proposes a field commissioning test method for distribution terminals, which builds a simulated test master station and imports distribution diagram data, constructs a communication system and test source on a mobile carrier, and realizes field commissioning test of terminal equipment. Most of these existing technologies adopt a centralized processing mode, relying on the master station to complete the message parsing and verification work, while the edge terminal mainly undertakes data acquisition and simple forwarding functions, forming a typical architecture of terminal acquisition-master station processing.

[0003] However, the most critical technical problem facing existing technologies lies in the system performance bottlenecks and scalability limitations caused by centralized processing architecture. As the scale of distribution automation systems continues to expand and the number of terminal devices grows exponentially, the traditional centralized processing mode requires the master station to process massive amounts of message data, resulting in severe computational load pressure, network bandwidth congestion, and response latency issues. Especially in scenarios with large-scale concurrent access from terminals, the processing capacity of the master station becomes the main bottleneck of system performance, failing to meet the stringent real-time requirements of joint debugging and testing. At the same time, the single master station architecture has the risk of single point of failure, severely restricting the reliability and availability of the system. This centralized architecture is difficult to adapt to the future development trend of distributed and intelligent distribution networks, and cannot effectively support the new requirements of edge computing and distributed collaborative processing, severely limiting the overall performance and scalability of distribution automation systems. Summary of the Invention

[0004] The purpose of this invention is to achieve intelligent parsing, security verification, and efficient processing of messages in the power Internet of Things, providing reliable data security and intelligent decision support for the power system. This invention proposes a message parsing and verification method for automatic commissioning of distribution terminals.

[0005] The technical solution of the present invention to solve the above-mentioned technical problems is as follows:

[0006] A method for message parsing and verification in automatic commissioning of power distribution terminals includes the following steps:

[0007] S1: Construct an intelligent message parsing system, and use a multimodal deep learning network to extract features and understand semantics of power distribution terminal messages to generate structured feature representations;

[0008] S2: Deploy a heterogeneous edge computing framework, integrate dedicated processing units on the power distribution terminal side, realize localized parsing and feature compression of message text, and optimize network transmission efficiency;

[0009] S3: Establish a decentralized verification network and ensure the verifiability and tamper-proof nature of the message verification process through a distributed consensus mechanism;

[0010] S4: Design an adaptive verification strategy engine to dynamically optimize verification rules and parameter configurations based on environmental awareness and historical data;

[0011] S5: Integrates physically unclonable function technology to generate unique device identifiers and verification credentials, enhancing identity authentication security;

[0012] S6: Build an intelligent diagnostic knowledge base to achieve intelligent identification of fault modes and generation of handling suggestions through correlation analysis and reasoning mechanisms.

[0013] Based on the above technical solution, the present invention can be further improved as follows.

[0014] Furthermore, the intelligent message parsing system in S1 includes:

[0015] A spatiotemporal convolutional neural network is used to extract the temporal features of the message, and a three-dimensional convolutional kernel is used to capture the time dimension and content dimension correlation features of the message data.

[0016] Design a hierarchical attention mechanism that assigns differentiated weights to the message header fields, data payload, and checksum, focusing on abnormal patterns and key parameters;

[0017] A variational autoencoder is constructed to compress features and learn the latent distribution representation of message data, thereby realizing the mapping of high-dimensional features to a low-dimensional semantic space.

[0018] Application domain adaptive transfer learning adapts a pre-trained general power system model to a specific power distribution scenario, and reduces the distribution differences between domains through adversarial training;

[0019] A sequence generative adversarial network is used to generate diverse abnormal samples. The model's generalization ability to unknown fault modes is improved through adversarial training between the generator and the discriminator.

[0020] A few-shot learning mechanism is introduced, which enables rapid model adaptation and support for new device access under conditions of few samples, based on prototype networks and metric learning.

[0021] Furthermore, the heterogeneous edge computing framework in S2 includes:

[0022] Deploy a neural network processing unit (NPU) in the power distribution terminal, which is dedicated to message feature extraction and preprocessing calculation, to improve processing efficiency and reduce power consumption.

[0023] A hierarchical federated learning architecture is adopted, in which edge nodes train lightweight models locally and periodically exchange model parameter updates with regional aggregation nodes;

[0024] Design a load-aware task scheduling algorithm to monitor the computing resources, network bandwidth, and energy status of each edge node in real time and dynamically allocate parsing tasks;

[0025] Establish Trusted Execution Environment (TEE) communication channels between edge nodes to ensure data confidentiality and integrity protection during distributed computing.

[0026] Deploy the parsing service using lightweight container orchestration technology to support the isolated operation and elastic scaling of multiple message format processing modules;

[0027] Implement an intelligent data caching mechanism to classify and store message templates based on access frequency and importance rating, thereby optimizing storage resource utilization;

[0028] Design an edge node health monitoring system to evaluate node status through multi-dimensional indicators, and achieve fault prediction and preventive maintenance.

[0029] Furthermore, the decentralized verification network in S3 includes:

[0030] Construct a distributed ledger structure based on a directed acyclic graph (DAG) to support parallel processing and fast confirmation of high-concurrency message verification transactions;

[0031] An improved practical Byzantine fault-tolerant algorithm is adopted, and a reputation mechanism is introduced for weighted voting to improve consensus efficiency and suppress the influence of malicious nodes;

[0032] Establish a multi-type smart contract library, including basic verification contracts, exception handling contracts, and strategy update contracts, supporting modular combination calls;

[0033] Implement cross-chain interoperability protocols, connect different power business blockchains through relay chain technology, and realize the trusted exchange and verification of verification data;

[0034] The fully homomorphic encryption scheme is applied, which supports performing verification calculations in the encrypted state to ensure end-to-end privacy protection of sensitive message content;

[0035] Design an incentive mechanism based on proof of contribution, and allocate system rewards and governance permissions according to the effective workload of nodes participating in verification;

[0036] It adopts a hierarchical storage architecture, where hot data is stored in a high-performance distributed database, cold data is archived to a low-cost storage system, and the blockchain only stores metadata hashes.

[0037] Furthermore, the adaptive verification strategy engine in S4 includes:

[0038] A partially observable Markov decision process (POMDP) ​​model is established to handle state uncertainty and observation noise during message verification.

[0039] A proximal strategy is adopted to optimize the PPO algorithm for training the verification policy network, and the stability of the training process is ensured by pruning the objective function.

[0040] Design a multi-objective optimization framework that balances multiple performance metrics such as verification accuracy, processing latency, and resource consumption.

[0041] By introducing a transfer reinforcement learning mechanism, the policy knowledge learned in other similar scenarios can be transferred to the current power distribution environment, thereby accelerating the learning process.

[0042] Build an environment dynamic perception module to monitor network conditions, device status and threat intelligence in real time, and dynamically adjust verification strategy parameters;

[0043] A course-based learning strategy is adopted, gradually transitioning from simple verification tasks to complex scenarios to improve the generalization ability of policy networks;

[0044] The objective function for optimizing the strategy is:

[0045]

[0046] in, Indicates policy network parameters, For trajectory sequence, As a discount factor, For state Next action Instant rewards express Divergence is used to constrain the policy update magnitude. This is the regularization coefficient.

[0047] Furthermore, the application of the physically unclonable function technique in S5 includes:

[0048] Utilizing the inherent physical differences in the chip manufacturing process to generate a unique digital fingerprint for a device, serving as an unclonable identity identifier;

[0049] Design a lightweight authentication protocol based on PUF to reduce the computational overhead and storage requirements of traditional cryptographic operations, making it suitable for resource-constrained environments;

[0050] Implement a response fuzzy extraction algorithm to handle noise and instability in PUF responses and ensure the reliability and consistency of key generation;

[0051] Establish a PUF response quality evaluation system and evaluate the randomness and uniqueness of PUF output by calculating Hamming distance and entropy value;

[0052] Develop a mechanism to prevent modeling attacks by dynamically updating challenge-response pairs and limiting access frequency to prevent PUF features from being modeled externally;

[0053] Design a multi-factor fusion authentication scheme that combines PUF identifiers, traditional cryptography, and biometrics to achieve multi-level identity verification;

[0054] It enables key lifecycle management, supporting secure management of the entire process of key generation, storage, update, and revocation based on PUF.

[0055] Furthermore, the intelligent diagnostic knowledge base in S6 includes:

[0056] Construct an ontology model for the power sector, formally defining the conceptual relationships and attribute constraints between power distribution equipment, fault types, and message characteristics;

[0057] A graph attention network (GAT) is used to learn representations of the knowledge graph, capturing important associations and semantic similarities between nodes;

[0058] Design a knowledge extraction pipeline to automatically extract fault knowledge triples from structured logs, semi-structured reports, and unstructured documents;

[0059] Implement an incremental knowledge update mechanism to continuously absorb new fault cases and diagnostic experience through online learning and proactive learning;

[0060] Establish a multi-source information fusion model to integrate real-time monitoring data, historical fault records, and expert experience and knowledge for comprehensive diagnosis;

[0061] Design an interpretable reasoning engine to provide reasoning paths and confidence assessments for diagnostic conclusions, thereby enhancing the credibility and usability of the results;

[0062] The semantic similarity calculation employs an improved graph neural network encoding:

[0063] in, This represents the encoding function of a graph neural network. This represents a vector concatenation operation. and For learnable parameters, This is the Sigmoid activation function.

[0064] Furthermore, the specific implementation of the spatiotemporal convolutional neural network includes:

[0065] Design a multi-scale convolutional kernel structure to capture both short-term local features and long-term global dependencies of the message data;

[0066] A deformable convolution module is introduced to adaptively adjust the receptive field of the convolution kernel, better adapting to variable-length message sequences and abnormal patterns;

[0067] By employing grouped convolution and depthwise separable convolution techniques, the number of model parameters and computational complexity are significantly reduced, making it suitable for edge deployment;

[0068] Implement a timing attention mechanism to dynamically focus on message features at key time steps and suppress noise and redundant information interference;

[0069] Design a dense residual connection structure to promote feature reuse and gradient flow, thereby alleviating the training difficulties of deep networks;

[0070] By applying neural architecture search technology, network structure and hyperparameter configuration are automatically optimized to improve the balance between model performance and efficiency.

[0071] Achieve model compression and quantization deployment, and meet the resource constraints of edge devices through knowledge distillation and low-bit quantization techniques.

[0072] Furthermore, the optimization strategies for the hierarchical federated learning architecture include:

[0073] Design a differentiated privacy budget allocation mechanism to dynamically adjust the differential privacy noise level based on data sensitivity and model importance;

[0074] Implement an asynchronous model aggregation algorithm to support heterogeneous nodes to submit updates in different time windows, thereby improving system flexibility and fault tolerance;

[0075] Develop gradient sparsity and quantization transmission techniques to transmit only important gradient updates, significantly reducing communication bandwidth consumption;

[0076] Establish a node reputation evaluation system to dynamically adjust aggregation weights and resource allocation based on historical contribution quality and participation.

[0077] Design a robust aggregation algorithm for federated learning, and use median estimation and gradient pruning methods to defend against poisoning attacks and anomalous updates;

[0078] Enables online monitoring of model performance, real-time evaluation of federated learning effectiveness, and timely adjustment of learning strategies and hyperparameter configurations;

[0079] Develop a cross-modal federated learning framework to support collaborative modeling and knowledge sharing of various types of power distribution terminal data.

[0080] Furthermore, the security mechanisms of the smart contract library include:

[0081] Implement a formal verification framework to ensure the logical correctness and security properties of smart contracts through theorem proving and model testing;

[0082] Design a contract vulnerability detection system and use a combination of static and dynamic analysis methods to identify potential security risks;

[0083] Establish a contract upgrade management mechanism to support canary releases and A / B testing, ensuring a smooth transition and rollback capability for contract updates;

[0084] Develop a Gas optimization compiler to reduce contract execution costs and resource consumption through code optimization and memory management;

[0085] Implement a multi-party contract audit process and introduce third-party security agencies to participate in contract code review and vulnerability discovery;

[0086] Design a contract access control model to finely control the calling permissions of contract functions based on role permissions and attribute policies;

[0087] Establish a contract execution monitoring and alarm system to detect abnormal transaction patterns and security events in real time and respond and handle them promptly.

[0088] Compared with the prior art, the technical solution of this application has the following beneficial technical effects:

[0089] This invention achieves accurate extraction and deep semantic understanding of message features through a multimodal deep learning network, overcoming the limitations of traditional parsing methods in adapting to complex messages and improving the intelligence level of parsing. The heterogeneous edge computing framework pushes message parsing and feature compression functions down to the power distribution terminal side, significantly reducing the amount of data that needs to be uploaded to the main station. This directly alleviates the massive data processing pressure and network bandwidth congestion faced by the main station under a centralized architecture, significantly reducing response latency and meeting the stringent real-time testing requirements. The decentralized verification network, through a distributed consensus mechanism, completely avoids the single point of failure risk of a single main station architecture, while ensuring verification... The verifiability and tamper-proof nature of the process enhance system reliability and scalability, aligning with the distributed and intelligent development trend of power distribution networks. The adaptive verification strategy engine dynamically adjusts verification rules and parameters based on environmental perception and historical data, solving the problem that traditional fixed rules are difficult to adapt to complex and ever-changing operating environments, and improving verification flexibility. Physically unclonable function technology generates unique device identifiers and verification credentials, strengthening identity authentication security and preventing the risks of device forgery and data tampering. The intelligent diagnostic knowledge base enables rapid identification of fault modes and generation of handling suggestions through correlation analysis and reasoning mechanisms, improving fault handling efficiency and reducing commissioning interruption time. Attached Figure Description

[0090] Figure 1 This is a flowchart illustrating the overall process of the method of the present invention.

[0091] Figure 2 This is a flowchart of the intelligent message parsing system of the present invention;

[0092] Figure 3 This is a flowchart of the heterogeneous edge computing framework of the present invention;

[0093] Figure 4 This is a flowchart of the decentralized verification network of the present invention;

[0094] Figure 5 This is a flowchart of the adaptive verification and diagnosis process of the present invention. Detailed Implementation

[0095] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0096] The present invention provides a method for message parsing and verification during automatic commissioning of power distribution terminals, comprising the following steps:

[0097] S1: Construct an intelligent message parsing system, and use a multimodal deep learning network to extract features and understand semantics of power distribution terminal messages to generate structured feature representations;

[0098] S2: Deploy a heterogeneous edge computing framework, integrate dedicated processing units on the power distribution terminal side, realize localized parsing and feature compression of message text, and optimize network transmission efficiency;

[0099] S3: Establish a decentralized verification network and ensure the verifiability and tamper-proof nature of the message verification process through a distributed consensus mechanism;

[0100] S4: Design an adaptive verification strategy engine to dynamically optimize verification rules and parameter configurations based on environmental awareness and historical data;

[0101] S5: Integrates physically unclonable function technology to generate unique device identifiers and verification credentials, enhancing identity authentication security;

[0102] S6: Build an intelligent diagnostic knowledge base to achieve intelligent identification of fault modes and generation of handling suggestions through correlation analysis and reasoning mechanisms.

[0103] like Figure 2 As shown, the intelligent message parsing system in S1 includes:

[0104] A spatiotemporal convolutional neural network is used to extract the temporal features of the message, and a three-dimensional convolutional kernel is used to capture the time dimension and content dimension correlation features of the message data.

[0105] Design a hierarchical attention mechanism that assigns differentiated weights to the message header fields, data payload, and checksum, focusing on abnormal patterns and key parameters;

[0106] A variational autoencoder is constructed to compress features and learn the latent distribution representation of message data, thereby realizing the mapping of high-dimensional features to a low-dimensional semantic space.

[0107] Application domain adaptive transfer learning adapts a pre-trained general power system model to a specific power distribution scenario, and reduces the distribution differences between domains through adversarial training;

[0108] A sequence generative adversarial network is used to generate diverse abnormal samples. The model's generalization ability to unknown fault modes is improved through adversarial training between the generator and the discriminator.

[0109] A few-shot learning mechanism is introduced, and rapid model adaptation and support for new device access are achieved under conditions of few samples based on prototype networks and metric learning. The spatiotemporal convolutional neural network adopts a three-layer convolutional structure. The first layer uses 32 convolutional kernels of size 3×3×3 with a stride of 1 to extract the basic spatiotemporal features of the message. The second layer is configured with 64 3×3×3 convolutional kernels, and the receptive field is expanded to 3 times the original size through dilated convolution technology. The third layer uses 128 1×1×1 convolutional kernels for feature compression and dimensionality reduction.

[0110] The hierarchical attention mechanism sets initial attention weights of 0.4, 0.5, and 0.1 for the message header, data payload, and checksum, respectively. These weights are dynamically adjusted based on feature importance using learnable parameters. Weight updates are implemented using the softmax function, with the specific formula as follows:

[0111]

[0112] : No. Attention weights for each field;

[0113] : A scoring function based on feature importance;

[0114] : No. The feature representation of each field;

[0115] The variational autoencoder employs a three-layer fully connected network structure for the encoder, compressing 1024-dimensional message features into a 128-dimensional latent space. The decoder uses a symmetric structure for feature reconstruction. The reconstruction loss function consists of two parts: reconstruction error and KL divergence, expressed as:

[0116]

[0117] : Reconstruction error term, which measures the difference between the reconstructed data and the original data;

[0118] KL divergence measures the difference between the latent spatial distribution and the prior distribution.

[0119] Balance parameter, set to 0.01;

[0120] : The posterior distribution of the encoder output;

[0121] Prior distribution (usually the standard normal distribution);

[0122] Domain-adaptive transfer learning employs the Maximum Mean Difference (MMD) metric, calculating the distance between source and target domain samples in the reproducing kernel Hilbert space to achieve inter-domain distribution alignment. Sequence generative adversarial networks utilize a Long Short-Term Memory (LSTM) network as the generator and a convolutional neural network as the discriminator, optimizing training stability using Wasserstein distance. Few-shot learning employs a prototype network mechanism, calculating the similarity between query samples and various prototypes based on Euclidean distance, supporting model learning under limited sample conditions.

[0123] like Figure 3 As shown, the heterogeneous edge computing framework in S2 includes:

[0124] Deploy a neural network processing unit (NPU) in the power distribution terminal, which is dedicated to message feature extraction and preprocessing calculation, to improve processing efficiency and reduce power consumption.

[0125] A hierarchical federated learning architecture is adopted, in which edge nodes train lightweight models locally and periodically exchange model parameter updates with regional aggregation nodes;

[0126] Design a load-aware task scheduling algorithm to monitor the computing resources, network bandwidth, and energy status of each edge node in real time and dynamically allocate parsing tasks;

[0127] Establish Trusted Execution Environment (TEE) communication channels between edge nodes to ensure data confidentiality and integrity protection during distributed computing.

[0128] Deploy the parsing service using lightweight container orchestration technology to support the isolated operation and elastic scaling of multiple message format processing modules;

[0129] Implement an intelligent data caching mechanism to classify and store message templates based on access frequency and importance rating, thereby optimizing storage resource utilization;

[0130] An edge node health monitoring system is designed to evaluate node status through multi-dimensional indicators, enabling fault prediction and preventative maintenance. The neural network processing unit uses the Huawei Ascend 310 chip, configured with an 8-core architecture, providing a peak computing power of 22 TOPS, with power consumption controlled within 8W, and supporting INT8 integer quantization inference. The hierarchical federated learning architecture is set up with a three-layer structure: edge layer (terminal device), fog layer (regional gateway), and cloud layer (central server), with model aggregation cycles of 10 minutes, 1 hour, and 24 hours for each layer, respectively.

[0131] A load-aware task scheduling algorithm monitors multiple performance metrics of nodes in real time, including CPU utilization. Memory usage Network bandwidth and remaining battery power The node status is evaluated using a weighted comprehensive scoring function.

[0132]

[0133] Among them, the weighting coefficient Set them to 0.3, 0.2, 0.3, and 0.2 respectively. Indicates the maximum bandwidth. Indicates the total power consumption;

[0134] The Trusted Execution Environment (TEX) uses Intel SGX technology, is configured with 128MB of secure memory, and supports the Remote Authentication Protocol (RA-TLS) to ensure secure communication. The lightweight container orchestration uses the K3s platform, with a single container memory limit of 256MB and a CPU share of 0.5 cores. It supports an automatic scaling strategy based on load metrics: automatically scaling up the number of instances when the average node load exceeds 80% and automatically scaling down when it falls below 30%.

[0135] The intelligent data cache adopts the LRU-K algorithm with K set to 2, improving the cache hit rate to over 85%. The edge node health monitoring system includes 10 key monitoring indicators: CPU temperature, memory error rate, disk SMART status, network packet loss rate, power stability, runtime, service response time, container restart count, number of security events, and log error frequency. The health status of nodes is evaluated through a weighted comprehensive scoring mechanism, with the weights dynamically adjusted according to the importance of the indicators.

[0136] like Figure 4 As shown, the decentralized verification network in S3 includes:

[0137] Construct a distributed ledger structure based on a directed acyclic graph (DAG) to support parallel processing and fast confirmation of high-concurrency message verification transactions;

[0138] An improved practical Byzantine fault-tolerant algorithm is adopted, and a reputation mechanism is introduced for weighted voting to improve consensus efficiency and suppress the influence of malicious nodes;

[0139] Establish a multi-type smart contract library, including basic verification contracts, exception handling contracts, and strategy update contracts, supporting modular combination calls;

[0140] Implement cross-chain interoperability protocols, connect different power business blockchains through relay chain technology, and realize the trusted exchange and verification of verification data;

[0141] The fully homomorphic encryption scheme is applied, which supports performing verification calculations in the encrypted state to ensure end-to-end privacy protection of sensitive message content;

[0142] Design an incentive mechanism based on proof of contribution, and allocate system rewards and governance permissions according to the effective workload of nodes participating in verification;

[0143] Employing a hierarchical storage architecture, hot data is stored in a high-performance distributed database, while cold data is archived to a low-cost storage system. The blockchain only stores metadata hashes. The directed acyclic graph distributed ledger adopts IOTA's Tangle structure, requiring verification of two preceding transactions for each new transaction, reducing transaction confirmation time to less than 1.5 seconds. An improved practical Byzantine fault-tolerant algorithm introduces a reputation weight mechanism based on proof-of-stake, with the node voting weight calculated using the following formula:

[0144]

[0145] :node Voting weight;

[0146] :node The number of tokens staked;

[0147] :node Historical correct voter turnout (0-1);

[0148] set up Control the extent of the impact on reputation;

[0149] The multi-type smart contract library contains three types of contracts: basic verification contracts (containing 20 predefined verification rules), exception handling contracts (containing 15 exception handling processes), and strategy update contracts (supporting dynamic updates of verification parameters). The code size of all contracts is controlled within 1MB. The cross-chain interoperability protocol adopts the Cosmos IBC protocol, achieving cross-chain transaction latency of less than 200ms and a throughput of 1000TPS.

[0150] The fully homomorphic encryption scheme uses the CKKS algorithm, supports floating-point calculations, keeps the ciphertext inflation factor within 10 times, and reduces the calculation precision loss to less than 0.001%. A contribution-based incentive mechanism is used to calculate the block reward formula.

[0151]

[0152] : Current block reward;

[0153] The initial block reward is set at 10 tokens.

[0154] Attenuation coefficient, set to 0.1;

[0155] : Current block height;

[0156] Half-life block count, set to 210000;

[0157] Additional transaction fees , For transaction size;

[0158] The tiered storage architecture is configured as follows: hot data storage uses a Redis cluster with a maximum capacity of 128GB; warm data storage uses Cassandra with a capacity of 1TB; cold data storage uses IPFS with unlimited capacity; and blockchain metadata uses LevelDB, which stores only 256-bit hash values.

[0159] like Figure 5 As shown, the adaptive verification strategy engine in S4 includes:

[0160] A partially observable Markov decision process (POMDP) ​​model is established to handle state uncertainty and observation noise during message verification.

[0161] A proximal strategy is adopted to optimize the PPO algorithm for training the verification policy network, and the stability of the training process is ensured by pruning the objective function.

[0162] Design a multi-objective optimization framework that balances multiple performance metrics such as verification accuracy, processing latency, and resource consumption.

[0163] By introducing a transfer reinforcement learning mechanism, the policy knowledge learned in other similar scenarios can be transferred to the current power distribution environment, thereby accelerating the learning process.

[0164] Build an environment dynamic perception module to monitor network conditions, device status and threat intelligence in real time, and dynamically adjust verification strategy parameters;

[0165] A course-based learning strategy is adopted, gradually transitioning from simple verification tasks to complex scenarios to improve the generalization ability of policy networks;

[0166] The objective function for strategy optimization is:

[0167]

[0168] in, Indicates policy network parameters, For trajectory sequence, As a discount factor, For state Next action Instant rewards express Divergence is used to constrain the policy update magnitude. For regularization coefficients, the state space of a partially observable Markov decision process contains 20 features, including: message type, data length, timestamp, source address, destination address, checksum, number of historical anomalies, network latency, CPU load, memory usage, battery level, signal strength, security level, priority, service type, encryption method, protocol version, geographical location, temperature, and humidity.

[0169] Setting pruning parameters in the near-end policy optimization algorithm Learning rate Discount factor GAE parameters Batch size 2048, training epoch 10; multi-objective optimization framework using weighted summation method, objective function is: ,in Indicates the accuracy of the verification. This indicates a delay in normalization processing. Represents resource consumption rate, weight ;

[0170] The transfer reinforcement learning mechanism uses policy distillation, with the teacher network employing a deep policy network trained in a simulated environment, and the student network being a lightweight network. The distillation temperature... KL divergence weights The environmental dynamic perception module monitors 10 environmental indicators: network bandwidth fluctuation, node online rate, attack frequency, data traffic, energy consumption, equipment temperature, weather conditions, load changes, security alarms, and system update status, with an update frequency of once per second.

[0171] The course learning strategy is set at 6 difficulty levels: Level 1 (normal packets from a single device), Level 2 (normal packets from multiple devices), Level 3 (abnormal packets from a single device), Level 4 (mixed packets from multiple devices), Level 5 (network attack simulation), and Level 6 (extreme environment conditions). Each level contains 10,000 training samples.

[0172] Applications of physically unclonable functions in S5 include:

[0173] Utilizing the inherent physical differences in the chip manufacturing process to generate a unique digital fingerprint for a device, serving as an unclonable identity identifier;

[0174] Design a lightweight authentication protocol based on PUF to reduce the computational overhead and storage requirements of traditional cryptographic operations, making it suitable for resource-constrained environments;

[0175] Implement a response fuzzy extraction algorithm to handle noise and instability in PUF responses and ensure the reliability and consistency of key generation;

[0176] Establish a PUF response quality evaluation system and evaluate the randomness and uniqueness of PUF output by calculating Hamming distance and entropy value;

[0177] Develop a mechanism to prevent modeling attacks by dynamically updating challenge-response pairs and limiting access frequency to prevent PUF features from being modeled externally;

[0178] Design a multi-factor fusion authentication scheme that combines PUF identifiers, traditional cryptography, and biometrics to achieve multi-level identity verification;

[0179] It implements key lifecycle management, supporting secure management of the entire process of key generation, storage, updating, and revocation based on PUF. Utilizing the physical characteristics of SRAM PUF, it reads the initial values ​​of 256 storage cells from the power-on state to generate a 256-bit original fingerprint. The lightweight authentication protocol employs a challenge-response mechanism, with challenge values... A 128-bit random number, response value The authentication success rate is greater than 99.9%;

[0180] The fuzzy response extraction uses the BCH(255,63,61) error correction code, which can correct up to 61 bits of error, and the information entropy reaches above 0.98. The PUF response quality assessment uses the NIST SP 800-22 test suite, including frequency test, intra-block frequency test, run test, matrix rank test, discrete Fourier test, non-overlapping module matching test, overlapping module matching test, general statistical test, linear complexity test, sequence test, approximate entropy test, summation test, and random walk test. All test items have a p-value greater than 0.01.

[0181] The anti-modeling attack mechanism sets the challenge-response pair update frequency to once per minute, limits access from a single IP address to 10 requests per second, and uses a hardware true random number generator for the challenge value randomness entropy source; multi-factor fusion authentication uses a weighted scoring mechanism. Weight Authentication threshold ;

[0182] The key lifecycle management settings include a key update cycle of 30 days and a key revocation list (CRL) update interval of 1 hour, supporting both forward and backward security guarantees.

[0183] The intelligent diagnostic knowledge base in S6 includes:

[0184] Construct an ontology model for the power sector, formally defining the conceptual relationships and attribute constraints between power distribution equipment, fault types, and message characteristics;

[0185] A graph attention network (GAT) is used to learn representations of the knowledge graph, capturing important associations and semantic similarities between nodes;

[0186] Design a knowledge extraction pipeline to automatically extract fault knowledge triples from structured logs, semi-structured reports, and unstructured documents;

[0187] Implement an incremental knowledge update mechanism to continuously absorb new fault cases and diagnostic experience through online learning and proactive learning;

[0188] Establish a multi-source information fusion model to integrate real-time monitoring data, historical fault records, and expert experience and knowledge for comprehensive diagnosis;

[0189] Design an interpretable reasoning engine to provide reasoning paths and confidence assessments for diagnostic conclusions, thereby enhancing the credibility and usability of the results;

[0190] Semantic similarity calculation employs an improved graph neural network encoding:

[0191]

[0192] in, This represents the encoding function of a graph neural network. This represents a vector concatenation operation. and For learnable parameters, Using the Sigmoid activation function, the power domain ontology model includes three main categories: equipment (56 concepts), faults (42 concepts), and features (38 concepts), defining 79 object attributes and 54 data attributes; the graph attention network adopts a two-layer structure, with the first layer having 8 attention heads and a hidden dimension of 256; and the second layer having 1 attention head and an output dimension of 128.

[0193] The formula for calculating the attention coefficient is:

[0194]

[0195] in For learnable parameter vectors, This is the weight matrix. and Representing nodes respectively and eigenvectors, Represents vector concatenation operation For nodes For nodes Attention coefficient For nodes The set of neighboring nodes;

[0196] The knowledge extraction pipeline consists of four modules: text preprocessing (word segmentation, part-of-speech tagging, named entity recognition), relation extraction (BERT-based relation classification), entity linking (Wikipedia-based disambiguation), and knowledge fusion (entity alignment based on similarity clustering), achieving an F1 score of 0.92. The incremental knowledge update mechanism is set to automatically update daily, with a new knowledge confidence threshold of 0.8. The conflict resolution strategy adopts a weighted voting method based on timestamps and source reliability.

[0197] Multi-source information fusion employs the DS evidence theory, with the basic probability allocation function being:

[0198]

[0199] Among them conflict factors , Assign values ​​to the basic probability of proposition A. A probability assignment function for different information sources. Propositions from different information sources;

[0200] The interpretable inference engine provides five interpretations: feature importance ranking (based on SHAP value), decision path display, similar case reference, rule triggering conditions, and confidence distribution.

[0201] The specific implementations of spatiotemporal convolutional neural networks include:

[0202] Design a multi-scale convolutional kernel structure to capture both short-term local features and long-term global dependencies of the message data;

[0203] A deformable convolution module is introduced to adaptively adjust the receptive field of the convolution kernel, better adapting to variable-length message sequences and abnormal patterns;

[0204] By employing grouped convolution and depthwise separable convolution techniques, the number of model parameters and computational complexity are significantly reduced, making it suitable for edge deployment;

[0205] Implement a timing attention mechanism to dynamically focus on message features at key time steps and suppress noise and redundant information interference;

[0206] Design a dense residual connection structure to promote feature reuse and gradient flow, thereby alleviating the training difficulties of deep networks;

[0207] By applying neural architecture search technology, network structure and hyperparameter configuration are automatically optimized to improve the balance between model performance and efficiency.

[0208] To achieve model compression and quantization deployment, knowledge distillation and low-bit quantization techniques are used to meet the resource constraints of edge devices. The multi-scale convolutional kernels are configured with three parallel branches: branch one uses a 1×1×1 convolutional kernel to extract local features; branch two uses a 3×3×3 convolutional kernel to capture medium-range features; and branch three uses a 5×5×5 dilated convolution (dilation=2) to obtain global contextual information. The outputs of the three branches are then fused using attention-weighted fusion. ,in: The characteristics after fusion The output features of the three branches, For attention weights, the weight parameters are automatically learned through the SE module.

[0209] Deformable convolution uses a 3×3×3 deformable convolution kernel with an offset learning rate of 0.1 and an offset range limited to ±2 pixels; grouped convolution is set to 32 groups; the computational cost ratio of depthwise separable convolution to pointwise convolution is 1:8, reducing the number of parameters by 75%; the temporal attention mechanism adopts a multi-head self-attention structure with 8 heads and 64 dimensions for query, key, and value.

[0210] The formula for calculating attention weights is: ,in

[0211] These represent the query, key, and value matrices, respectively. The dimension of the key vector is represented; the residual dense connection adopts the DenseNet-BC structure, with a growth rate k=32 and a compression coefficient θ=0.5. Each dense block contains 6 convolutional layers, and the transition layer uses 1×1 convolution and 2×2 average pooling.

[0212] The neural architecture search employs the ENAS algorithm, with a search space containing 15 convolution operations, 8 pooling operations, and 6 activation functions, and a search period of 50 epochs. Model compression utilizes knowledge distillation, achieving an accuracy of 98.5% for the teacher model and 96.2% for the student model, while reducing model size by a factor of 4. Quantization training employs the QAT method, with 8-bit quantization for weights and 4-bit quantization for activation values, resulting in a 2.3-fold increase in inference speed.

[0213] Optimization strategies for hierarchical federated learning architectures include:

[0214] Design a differentiated privacy budget allocation mechanism to dynamically adjust the differential privacy noise level based on data sensitivity and model importance;

[0215] Implement an asynchronous model aggregation algorithm to support heterogeneous nodes to submit updates in different time windows, thereby improving system flexibility and fault tolerance;

[0216] Develop gradient sparsity and quantization transmission techniques to transmit only important gradient updates, significantly reducing communication bandwidth consumption;

[0217] Establish a node reputation evaluation system to dynamically adjust aggregation weights and resource allocation based on historical contribution quality and participation.

[0218] Design a robust aggregation algorithm for federated learning, and use median estimation and gradient pruning methods to defend against poisoning attacks and anomalous updates;

[0219] Enables online monitoring of model performance, real-time evaluation of federated learning effectiveness, and timely adjustment of learning strategies and hyperparameter configurations;

[0220] Develop a cross-modal federated learning framework to support collaborative modeling and knowledge sharing of various types of power distribution terminal data, and set up a three-tiered privacy protection level with differentiated privacy budget allocation: Level 1 (sensitive data) privacy budget. Level 2 (General Data) Level 3 (public data) Noise addition uses a Gaussian mechanism. ,in , Indicates function sensitivity; , Indicates privacy budget parameters;

[0221] The asynchronous model aggregation is set with a time window of 10 minutes, a maximum latency tolerance of 30 minutes, and an obsolescence weight decay factor. , The number of epochs is the delay; gradient sparsity uses Top-k selection, retaining the top 10% of important gradients, and quantization transmission uses 8-bit uniform quantization, reducing the communication volume to 12.5% ​​of the original.

[0222] Node reputation assessment is based on five dimensions: data quality (40% weight), computational stability (25%), participation (15%), security record (10%), and contribution value (10%). The reputation score is calculated using the following formula: , Reputation score; Weights for each dimension; Each dimension is scored (0-1), and nodes scoring below 60 are suspended from participating; the federated learning robust aggregation uses the Krum algorithm to select the parameters most consistent with other updates;

[0223] Model performance monitoring sets early stopping conditions: if the accuracy improvement on the validation set is less than 0.1% for 10 consecutive rounds, the learning rate decay coefficient is 0.5, and the minimum learning rate is limited to 1e-6; cross-modal federated learning supports 4 data types: structured data (database records), semi-structured data (XML / JSON), unstructured data (text logs), and time series data (sensor readings).

[0224] The security mechanisms of smart contract libraries include:

[0225] Implement a formal verification framework to ensure the logical correctness and security properties of smart contracts through theorem proving and model testing;

[0226] Design a contract vulnerability detection system and use a combination of static and dynamic analysis methods to identify potential security risks;

[0227] Establish a contract upgrade management mechanism to support canary releases and A / B testing, ensuring a smooth transition and rollback capability for contract updates;

[0228] Develop a Gas optimization compiler to reduce contract execution costs and resource consumption through code optimization and memory management;

[0229] Implement a multi-party contract audit process and introduce third-party security agencies to participate in contract code review and vulnerability discovery;

[0230] Design a contract access control model to finely control the calling permissions of contract functions based on role permissions and attribute policies;

[0231] A contract execution monitoring and alerting system was established to detect abnormal transaction patterns and security events in real time and respond and handle them promptly. The formal verification framework adopted the Coq proof assistant and defined eight security attributes: termination, no deadlock, no asset loss, access control compliance, complete input validation, complete exception handling, state consistency, and no reentrancy vulnerabilities. Contract vulnerability detection combined static analysis (Slither tool) and dynamic analysis (Mythril tool) to detect 28 common types of vulnerabilities.

[0232] Contract upgrade management adopts a proxy model, separating logical contracts from storage. The upgrade process is controlled by multi-signature contracts and requires approval from at least 3 out of 5 administrators. The gas optimization compiler uses Yul intermediate representation, and optimization strategies include: inlining small functions (saving 200-500 gas), using bytes32 instead of strings (saving 50% of storage costs), batch operations (saving 21,000 gas in transaction base fees), and event log optimization (using indexed parameters).

[0233] The multi-party contract audit process consists of four phases: automatic scanning (days 1-2), manual auditing (days 3-7), remediation and verification (days 8-10), and final report (day 11). The audit standard follows ISO / IEC 27001. Contract access control adopts the RBAC model, defining five roles: super administrator, contract administrator, auditor, operator, and read-only user, with permission granularity controlled down to the function level.

[0234] Contract execution monitoring is configured with anomaly detection rules: abnormal gas consumption (exceeding the estimate by 200%), frequent calls (more than 100 times within 1 minute), calls at abnormal times (outside of working hours), privilege escalation attempts, reentrancy attack mode, and alarm response time of less than 30 seconds.

[0235] The spatiotemporal convolutional neural network extracts deep features from the input message. The network adopts a three-layer convolutional structure to capture basic features, expand the receptive field, and compress features. The hierarchical attention mechanism dynamically adjusts the attention to different message fields, prioritizes key information, and the variational autoencoder compresses high-dimensional features into the latent space, retaining key information while reducing computational complexity.

[0236] At the edge computing layer, the system adopts a heterogeneous computing architecture, equipped with a dedicated neural network processing unit, providing high-performance and low-power computing capabilities. The hierarchical federated learning framework enables collaborative model training among edge devices, regional gateways, and cloud servers. Differential privacy technology protects data security, and a load-aware task scheduling algorithm monitors node status in real time and intelligently allocates computing tasks to ensure stable system operation.

[0237] The decentralized verification network adopts a directed acyclic graph ledger structure to achieve fast transaction confirmation and high throughput. The improved Byzantine fault-tolerant algorithm, combined with proof-of-stake and reputation mechanisms, ensures the security and efficiency of the consensus process. Multiple types of smart contracts provide flexible verification rules and exception handling processes, and support dynamic updates and cross-chain interoperability.

[0238] The adaptive verification strategy engine is based on a partially observable Markov decision process, comprehensively considers 20-dimensional environmental features, and achieves intelligent decision-making through a near-end policy optimization algorithm. The multi-objective optimization framework balances verification accuracy, processing latency and resource consumption, ensuring the optimal performance of the system in different scenarios.

[0239] Physically unclonable function technology provides hardware-level security authentication, using chip physical characteristics to generate a unique device identifier. Lightweight authentication protocols and response fuzzy extraction mechanisms ensure the security and reliability of the authentication process. Multi-factor fusion authentication combines multiple authentication methods to provide multi-layered security.

[0240] The intelligent diagnostic knowledge base constructs an ontology model in the power field, realizes knowledge reasoning and association analysis through graph attention networks, integrates various data sources through multi-source information fusion technology, provides comprehensive fault diagnosis and analysis capabilities, and provides a transparent display of the decision-making process through an interpretable reasoning engine, enhancing the credibility of the system.

[0241] The system optimizes the network structure through neural architecture search and model compression techniques to balance accuracy and computational efficiency. Knowledge distillation and quantization training further reduce model complexity and improve inference speed. Hierarchical federated learning supports multimodal data processing and enables knowledge transfer across devices and scenarios.

[0242] The smart contract library employs formal verification and multiple auditing mechanisms to ensure contract security and reliability. The Gas-optimized compiler reduces execution costs and improves system economy. Real-time monitoring and anomaly detection mechanisms provide comprehensive security guarantees to ensure stable system operation.

[0243] Through close collaboration among its modules, the entire system enables intelligent parsing, security verification, and efficient processing of messages from the power Internet of Things, providing reliable data security and intelligent decision support for the power system.

[0244] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0245] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for message parsing and verification in automatic commissioning of power distribution terminals, characterized in that, Includes the following steps: S1: Construct an intelligent message parsing system, and use a multimodal deep learning network to extract features and understand semantics of power distribution terminal messages to generate structured feature representations; S2: Deploy a heterogeneous edge computing framework, integrate dedicated processing units on the power distribution terminal side, realize localized parsing and feature compression of message text, and optimize network transmission efficiency; S3: Establish a decentralized verification network and ensure the verifiability and tamper-proof nature of the message verification process through a distributed consensus mechanism; S4: Design an adaptive verification strategy engine to dynamically optimize verification rules and parameter configurations based on environmental awareness and historical data; S5: Integrates physically unclonable function technology to generate unique device identifiers and verification credentials, enhancing identity authentication security; S6: Build an intelligent diagnostic knowledge base to achieve intelligent identification of fault modes and generation of handling suggestions through correlation analysis and reasoning mechanisms.

2. The method for message parsing and verification in automatic commissioning of power distribution terminals according to claim 1, characterized in that, The intelligent message parsing system in S1 includes: A spatiotemporal convolutional neural network is used to extract the temporal features of the message, and a three-dimensional convolutional kernel is used to capture the time dimension and content dimension correlation features of the message data. Design a hierarchical attention mechanism to assign differentiated weights to the message header fields, data payload, and checksum, focusing on abnormal patterns and key parameters; A variational autoencoder is constructed to compress features and learn the latent distribution representation of message data, thereby realizing the mapping of high-dimensional features to a low-dimensional semantic space. Application domain adaptive transfer learning adapts a pre-trained general power system model to a specific power distribution scenario, and reduces the distribution differences between domains through adversarial training; A sequence generative adversarial network is used to generate diverse abnormal samples. The model's generalization ability to unknown fault modes is improved through adversarial training between the generator and the discriminator. A few-shot learning mechanism is introduced, which enables rapid model adaptation and support for new device access under conditions of few samples, based on prototype networks and metric learning.

3. The method for message parsing and verification in automatic commissioning of a power distribution terminal according to claim 1, characterized in that, The heterogeneous edge computing framework in S2 includes: Deploy a neural network processing unit (NPU) in the power distribution terminal, which is dedicated to message feature extraction and preprocessing calculation, to improve processing efficiency and reduce power consumption. A hierarchical federated learning architecture is adopted, in which edge nodes train lightweight models locally and periodically exchange model parameter updates with regional aggregation nodes; Design a load-aware task scheduling algorithm to monitor the computing resources, network bandwidth, and energy status of each edge node in real time and dynamically allocate parsing tasks; Establish Trusted Execution Environment (TEE) communication channels between edge nodes to ensure data confidentiality and integrity protection during distributed computing. Deploy the parsing service using lightweight container orchestration technology to support the isolated operation and elastic scaling of multiple message format processing modules; Implement an intelligent data caching mechanism to classify and store message templates based on access frequency and importance rating, thereby optimizing storage resource utilization; Design an edge node health monitoring system to evaluate node status through multi-dimensional indicators, and achieve fault prediction and preventive maintenance.

4. The method for message parsing and verification in automatic commissioning of a power distribution terminal according to claim 1, characterized in that, The decentralized verification network in S3 includes: Construct a distributed ledger structure based on a directed acyclic graph (DAG) to support parallel processing and fast confirmation of high-concurrency message verification transactions; An improved practical Byzantine fault-tolerant algorithm is adopted, and a reputation mechanism is introduced for weighted voting to improve consensus efficiency and suppress the influence of malicious nodes; Establish a multi-type smart contract library, including basic verification contracts, exception handling contracts, and strategy update contracts, supporting modular combination calls; Implement cross-chain interoperability protocols, connect different power business blockchains through relay chain technology, and realize the trusted exchange and verification of verification data; The fully homomorphic encryption scheme is applied, which supports performing verification calculations in the encrypted state to ensure end-to-end privacy protection of sensitive message content; Design an incentive mechanism based on proof of contribution, and allocate system rewards and governance permissions according to the effective workload of nodes participating in verification; It adopts a hierarchical storage architecture, where hot data is stored in a high-performance distributed database, cold data is archived to a low-cost storage system, and the blockchain only stores metadata hashes.

5. The method for message parsing and verification in automatic commissioning of a power distribution terminal according to claim 1, characterized in that, The adaptive verification strategy engine in S4 includes: A partially observable Markov decision process (POMDP) ​​model is established to handle state uncertainty and observation noise during message verification. A proximal strategy is adopted to optimize the PPO algorithm for training the verification policy network, and the stability of the training process is ensured by pruning the objective function. Design a multi-objective optimization framework that balances multiple performance metrics such as verification accuracy, processing latency, and resource consumption. By introducing a transfer reinforcement learning mechanism, the policy knowledge learned in other similar scenarios can be transferred to the current power distribution environment, thereby accelerating the learning process. Build an environment dynamic perception module to monitor network conditions, device status and threat intelligence in real time, and dynamically adjust verification strategy parameters; A course-based learning strategy is adopted, gradually transitioning from simple verification tasks to complex scenarios to improve the generalization ability of policy networks; The objective function for optimizing the strategy is: ； in, Indicates the policy network parameters, For trajectory sequence, As a discount factor, For state Next action Instant rewards express Divergence is used to constrain the magnitude of policy updates. is the regularization coefficient.

6. The method for message parsing and verification in automatic commissioning of a power distribution terminal according to claim 1, characterized in that, The application of the physically unclonable function technique in S5 includes: Utilizing the inherent physical differences in the chip manufacturing process to generate a unique digital fingerprint for a device, serving as an unclonable identity identifier; Design a lightweight authentication protocol based on PUF to reduce the computational overhead and storage requirements of traditional cryptographic operations, making it suitable for resource-constrained environments; Implement a response fuzzy extraction algorithm to handle noise and instability in PUF responses and ensure the reliability and consistency of key generation; Establish a PUF response quality evaluation system and evaluate the randomness and uniqueness of PUF output by calculating Hamming distance and entropy value; Develop a mechanism to prevent modeling attacks by dynamically updating challenge-response pairs and limiting access frequency to prevent PUF features from being modeled externally; Design a multi-factor fusion authentication scheme that combines PUF identifiers, traditional cryptography, and biometrics to achieve multi-level identity verification; It enables key lifecycle management, supporting secure management of the entire process of key generation, storage, update, and revocation based on PUF.

7. The method for message parsing and verification in automatic commissioning of a power distribution terminal according to claim 1, characterized in that, The intelligent diagnostic knowledge base in S6 includes: Construct an ontology model for the power sector, formally defining the conceptual relationships and attribute constraints between power distribution equipment, fault types, and message characteristics; A graph attention network (GAT) is used to learn representations of the knowledge graph, capturing important associations and semantic similarities between nodes; Design a knowledge extraction pipeline to automatically extract fault knowledge triples from structured logs, semi-structured reports, and unstructured documents; Implement an incremental knowledge update mechanism to continuously absorb new fault cases and diagnostic experience through online learning and proactive learning; Establish a multi-source information fusion model to integrate real-time monitoring data, historical fault records, and expert experience and knowledge for comprehensive diagnosis; Design an interpretable reasoning engine to provide reasoning paths and confidence assessments for diagnostic conclusions, thereby enhancing the credibility and usability of the results; The semantic similarity calculation employs an improved graph neural network encoding: ； in, This represents the encoding function of a graph neural network. This represents a vector concatenation operation. and For learnable parameters, This is the Sigmoid activation function.

8. The method for message parsing and verification in automatic commissioning of a power distribution terminal according to claim 2, characterized in that, The specific implementation of the spatiotemporal convolutional neural network includes: Design a multi-scale convolutional kernel structure to capture both short-term local features and long-term global dependencies of the message data; A deformable convolution module is introduced to adaptively adjust the receptive field of the convolution kernel, better adapting to variable-length message sequences and abnormal patterns; By employing grouped convolution and depthwise separable convolution techniques, the number of model parameters and computational complexity are significantly reduced, making it suitable for edge deployment; Implement a timing attention mechanism to dynamically focus on message features at key time steps and suppress noise and redundant information interference; Design a dense residual connection structure to promote feature reuse and gradient flow, thereby alleviating the training difficulties of deep networks; By applying neural architecture search technology, network structure and hyperparameter configuration are automatically optimized to improve the balance between model performance and efficiency. Achieve model compression and quantization deployment, and meet the resource constraints of edge devices through knowledge distillation and low-bit quantization techniques.

9. The method for message parsing and verification in automatic commissioning of a power distribution terminal according to claim 3, characterized in that, The optimization strategies for the hierarchical federated learning architecture include: Design a differentiated privacy budget allocation mechanism to dynamically adjust the differential privacy noise level based on data sensitivity and model importance; Implement an asynchronous model aggregation algorithm to support heterogeneous nodes to submit updates in different time windows, thereby improving system flexibility and fault tolerance; Develop gradient sparsity and quantization transmission techniques to transmit only important gradient updates, significantly reducing communication bandwidth consumption; Establish a node reputation evaluation system to dynamically adjust aggregation weights and resource allocation based on historical contribution quality and participation. Design a robust aggregation algorithm for federated learning, and use median estimation and gradient pruning methods to defend against poisoning attacks and anomalous updates; Enables online monitoring of model performance, real-time evaluation of federated learning effectiveness, and timely adjustment of learning strategies and hyperparameter configurations; Develop a cross-modal federated learning framework to support collaborative modeling and knowledge sharing of various types of power distribution terminal data.

10. The method for message parsing and verification in automatic commissioning of a power distribution terminal according to claim 4, characterized in that, The security mechanisms of the smart contract library include: Implement a formal verification framework to ensure the logical correctness and security properties of smart contracts through theorem proving and model testing; Design a contract vulnerability detection system and use a combination of static and dynamic analysis methods to identify potential security risks; Establish a contract upgrade management mechanism to support canary releases and A / B testing, ensuring a smooth transition and rollback capability for contract updates; Develop a Gas optimization compiler to reduce contract execution costs and resource consumption through code optimization and memory management; Implement a multi-party contract audit process and introduce third-party security agencies to participate in contract code review and vulnerability discovery; Design a contract access control model to finely control the calling permissions of contract functions based on role permissions and attribute policies; Establish a contract execution monitoring and alarm system to detect abnormal transaction patterns and security events in real time and respond and handle them promptly.

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