Blockchain-based power transaction terminal data management and transaction settlement optimization method

Abstract

The application belongs to the technical field of electric power transaction data processing, and relates to a blockchain-based electric power transaction terminal data management and transaction settlement optimization method, which comprises the following steps: acquiring global block state data, and mapping to generate a physical sweep frequency parameter set; controlling an aggregator to inject a high-frequency excitation signal into a distribution network and receive a power grid resonance response signal; capturing a signal by a terminal to extract an instantaneous transient coefficient matrix; summarizing electric quantity metering records, and generating a settlement high-order polynomial through algebraic coding; merging matrices to construct a global feature tensor and reduce dimensions, and outputting evaluation point parameters; substituting the evaluation point into a polynomial to construct a polynomial commitment value and generate a zero-knowledge proof, and assembling the zero-knowledge proof into a transaction instruction for chaining; triggering a smart contract to call an oracle to generate a benchmark transient reference space; and analyzing the instruction to determine whether the evaluation point falls into the space to execute settlement. The application solves the problem that existing blockchain-based electric power transaction systems lack physical layer source end cross verification of chained transaction data.

Description

Technical Field

[0001] This invention belongs to the technical field of power trading data processing, and relates to a blockchain-based method for optimizing power trading terminal data management and transaction settlement. Background Technology

[0002] Currently, with the rise of distributed energy and the advancement of power market reform, new power trading models are gradually increasing, placing higher demands on the real-time performance, transparency, and security of transactions. Traditional centralized power trading and settlement systems mainly rely on central servers, which pose a risk of single point of failure and involve cumbersome cross-institutional data reconciliation processes. Furthermore, the transparency of the system's transaction settlement process is limited, making it difficult for users to trace each stage of electricity bill calculation. In addition, if the data from the electricity metering terminal is subjected to external interference during collection or transmission, the centralized system struggles to effectively verify the physical authenticity of the data, easily leading to discrepancies in billing and settlement.

[0003] To address the shortcomings of centralized systems in data management and settlement transparency, existing technologies utilize blockchain technology to build decentralized power trading platforms. These solutions store power trading data as transaction records in a distributed ledger, leveraging the immutability and traceability of blockchain data to enhance the transparency of the trading process and the security of on-chain data. For example, Chinese invention patent application number CN118966590A discloses a blockchain-based power trading terminal trust management method and system. These solutions establish immutable accounting vouchers by recording terminal transaction data on the blockchain, thereby enhancing the credibility of transaction records.

[0004] However, while the aforementioned existing technologies ensure stable storage of on-chain information through data uploading, they still have certain technical limitations in ensuring the authenticity of the data source. The anti-tampering mechanisms of such blockchain solutions are mainly concentrated on the on-chain stage, and their overall security largely depends on the initial trustworthiness of the data collection terminal. In practical applications, if terminal devices such as smart meters suffer hardware damage, firmware modification, or communication link interference, the system finds it difficult to directly intercept abnormal interventions at the underlying level and will still write the submitted abnormal electricity consumption data as a valid record into the block. Existing technologies lack cross-verification methods for the physical authenticity of off-chain data, posing a risk of false data being uploaded to the blockchain, thereby affecting the final fairness of transaction settlement and the overall security level of the system. Summary of the Invention

[0005] To address the aforementioned problems, this invention provides a blockchain-based method for optimizing data management and transaction settlement in power trading terminals.

[0006] A blockchain-based method for optimizing power trading terminal data management and transaction settlement includes the following steps: S1. Obtain global block state data from the blockchain mainnet and generate a physical frequency sweep parameter set for the local power distribution network through a deterministic mapping mechanism; S2. The control area aggregator injects high-frequency excitation signals into the associated distribution network topology circuit based on the physical frequency sweep parameter set, and receives the grid resonance response signal reflected by the line impedance. S3. Control the designated power terminal to capture the grid resonance response signal, and use discrete wavelet transform to extract the instantaneous transient coefficient matrix that can characterize the electromagnetic characteristics of the terminal. S4. Summarize the electricity metering records reported by the power terminals during the settlement period, and generate a higher-order polynomial for the settlement that maps the settlement relationship through algebraic encoding conversion; S5. Merge multiple instantaneous transient coefficient matrices to construct a global feature tensor, use an orthogonal model for dimensionality reduction projection, and output the polynomial evaluation point parameters. S6. Substitute the polynomial evaluation point parameters into the settlement higher-order polynomial for calculation to construct the polynomial commitment value, generate a zero-knowledge proof, and assemble the polynomial commitment value, the zero-knowledge proof, and the accumulated total transaction volume into a polynomial commitment aggregated transaction instruction and send it to the blockchain mainnet. S7. Obtain the multinomial commitment aggregation transaction instruction through the system consensus node and trigger the smart contract. Call the oracle to update the impedance parameters of the virtual distribution network model by combining the accumulated total electricity in the multinomial commitment aggregation transaction instruction, and replicate the benchmark transient reference space. S8. Extract zero-knowledge proofs from the parsed polynomial commitment aggregation transaction instructions, determine whether the evaluation point submitted as public input parameters along with the zero-knowledge proof falls within the benchmark transient reference space, and execute settlement after verification.

[0007] A further aspect of the present invention, step S1, includes the following steps: Read the block header information at the start of the current settlement time window, and extract the block header random number and global state root as global block state data; The hash mapping algorithm is called to combine the random number in the block header and the global state root to calculate the initial sweep parameters, which include the center frequency and bandwidth parameters. Based on the structural topology characteristics of the local power distribution network, the initial frequency sweep parameters are adjusted by phase offset to generate a physical frequency sweep parameter set containing the center frequency, bandwidth, and target phase offset.

[0008] A further aspect of the present invention, step S2, includes the following steps: High-frequency analog sweep pulses corresponding to the physical sweep frequency parameter set are generated through digital-to-analog conversion processing. The power drive unit of the control area aggregator couples high-frequency analog sweep pulses to the distribution network to generate high-frequency excitation signals; The high-frequency excitation signal is reflected by the impedance of the distribution network line to generate the power grid resonant response signal.

[0009] A further aspect of the present invention, step S3, includes the following steps: Capture the grid resonance response signal and line reflection noise within a preset timing window; Discrete wavelet transform is used to process the power grid resonance response signal and line reflection clutter, and the detail coefficients under the ultra-high frequency subband are extracted to generate the damped oscillation energy envelope distribution. Spatial feature vectors are extracted based on the energy envelope distribution of the damped oscillations, and then the spatial feature vectors are converted into a multi-dimensional array to construct the instantaneous transient coefficient matrix.

[0010] A further aspect of the present invention, step S4, includes the following steps: Obtain valid electricity metering records of power terminals within the settlement period and identify the terminal identity identifier associated with the valid electricity metering records; The valid electricity metering records associated with each terminal's identity are mapped proportionally to polynomial coefficients. Based on the coefficients of each polynomial, perform algebraic recombination multiplication to generate a higher-order polynomial representing the total global electricity generation and consumption.

[0011] A further aspect of the present invention, step S5, includes the following steps: Obtain the instantaneous transient coefficient matrices reported by each power terminal, and stack them according to the network topology order to synthesize a global feature tensor; Principal component analysis is used to perform dimensionality reduction projection on the global feature tensor, and the output is a feature fusion scalar that indicates the spatiotemporal coupling status of physical interactions. Perform boundary normalization operations on the feature fusion scalar to generate polynomial evaluation point parameters.

[0012] A further aspect of the present invention, step S6, includes the following steps: Substitute the polynomial evaluation point parameters into the settlement higher-order polynomial to perform scalar function evaluation, and generate polynomial commitment values ​​based on the quotient polynomial. The zero-knowledge proof compiler is used to perform a validity calculation on the polynomial commitment value and output the zero-knowledge proof. The polynomial commitment value, zero-knowledge proof, polynomial evaluation point parameters (which are public input parameters), and the total accumulated transaction volume are serialized and packaged to generate a polynomial commitment aggregation transaction instruction.

[0013] A further aspect of the present invention, step S7, includes the following steps: By listening to blockchain transactions to obtain multinomial commitment aggregation transaction instructions and triggering smart contracts, block records are queried in reverse order to reconstruct the physical sweep frequency parameter set; The oracle is invoked to load the physical sweep frequency parameter set, and the equivalent load impedance is calculated by combining the total electricity of the accumulated transaction in the polynomial commitment aggregation transaction instruction to update the virtual distribution network model. The theoretical electromagnetic wave propagation behavior is simulated in the updated virtual distribution network model. The set of impedance response distributions of the reference nodes received from the oracle is used to construct a reference transient reference space that characterizes normal physical energy consumption.

[0014] A further aspect of the present invention, step S8, includes the following steps: By parsing polynomial commitments and aggregating transaction instructions through smart contracts, the polynomial commitment value and zero-knowledge proof are extracted. Call the smart contract's built-in parser to determine whether the evaluation point submitted as a public input parameter along with the zero-knowledge proof falls within the baseline transient reference space; If the transaction is successful, the blockchain ledger module will be triggered, and the total transaction amount carried by the aggregated transaction instructions based on the polynomial commitment will be used to settle funds in the digital wallets of each end user.

[0015] A further aspect of this invention involves settling funds with the digital wallets of various end users, including the following steps: The total electricity consumption is extracted from the aggregated transaction instructions of the multinomial commitment through smart contracts, and the total electricity cost is calculated by combining it with the real-time electricity price provided by the oracle. The total electricity cost will be allocated to an on-chain escrow address controlled by the regional aggregator. The regional aggregator initiates transfers to the digital wallets of each terminal user off-chain based on the electricity metering records of each terminal it holds, in proportion to complete the settlement.

[0016] In summary, the present invention has the following beneficial technical effects: 1. This invention maps the consensus state random numbers of the blockchain to physical frequency sweep parameters and controls regional aggregators to inject high-frequency excitation signals into the power distribution network, causing power terminals to generate transient response signals related to their internal circuits and line impedance, etc. Then, it extracts the instantaneous transient features of each terminal and performs dimensionality reduction projection to generate polynomial evaluation point parameters representing the current physical state of the power grid. This mechanism establishes a correlation between the input variables of subsequent cryptographic verification and the actual physical hardware attributes of the power terminal, which are difficult to replicate. Compared to purely digital verification mechanisms, this adds a layer of physical authenticity verification. When a terminal is tampered with at the data acquisition or transmission layer, the forged data is unlikely to generate evaluation point parameters that match the real physical state of the power grid, making it easier to identify during on-chain verification and improving the power trading system's protection against data source deception attacks.

[0017] 2. This invention employs a processing method that encodes the effective electricity metering records of each terminal into polynomial coefficients, reorganizing the dispersed electricity settlement data into a single high-order settlement polynomial. This is combined with polynomial commitment and zero-knowledge proof technology to generate aggregated transaction instructions for on-chain processing. This mechanism allows blockchain nodes to verify the aggregated settlement data without needing to parse the specific electricity consumption details of individual terminals; they only need to perform a legality check on the zero-knowledge proof to confirm the correctness of the aggregated settlement result. This polynomial-based aggregation verification method protects the electricity consumption privacy of terminal nodes while effectively reducing the computational overhead and data storage pressure on the blockchain mainnet when facing concurrent settlements from multiple terminals. This helps improve the operational efficiency and system scalability of batch settlements in distributed transaction scenarios.

[0018] 3. This invention designs a collaborative verification architecture combining on-chain smart contracts and off-chain oracles. Within the smart contract, the incentive parameters of physical detection are reconstructed, and the oracle is invoked to dynamically update the load impedance of the virtual power distribution network model based on the total transaction electricity declared by the terminal, and simulation is performed to construct a benchmark transient reference space that matches the declared energy consumption state. This benchmark space provides a dynamic comparison interval based on both physical and electrical constraints for the evaluation points measured and submitted off-chain. The smart contract automatically verifies the physical credibility of the transaction and performs subsequent fund settlement by determining whether the actual evaluation point falls within this reference space. This scheme combines cryptographic verification in the digital space with simulation comparison in the real physical environment, forming an automated closed-loop settlement logic. This reduces the reliance on manual verification mechanisms in the settlement arbitration process, which is beneficial to ensuring the objectivity and security of the automatic settlement process for electricity transactions. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. The drawings are used to provide a further understanding of the present invention.

[0020] Figure 1 A flowchart illustrating an embodiment of this application is disclosed.

[0021] Figure 2 Structural schematic diagrams of embodiments of this application are disclosed. Detailed Implementation

[0022] The following is in conjunction with the appendix Figures 1-2 A preferred description of the present invention is provided below.

[0023] See attached document Figure 1 This invention proposes a blockchain-based method for optimizing data management and transaction settlement in power trading terminals, comprising the following steps: S1. Obtain global block state data from the blockchain mainnet and generate a physical frequency sweep parameter set for the local power distribution network through a deterministic mapping mechanism; S2. The control area aggregator injects high-frequency excitation signals into the associated distribution network topology circuit based on the physical frequency sweep parameter set, and receives the grid resonance response signal reflected by the line impedance. S3. Control the designated power terminal to capture the grid resonance response signal, and use discrete wavelet transform to extract the instantaneous transient coefficient matrix that can characterize the electromagnetic characteristics of the terminal. S4. Summarize the electricity metering records reported by the power terminals during the settlement period, and generate a higher-order polynomial for the settlement that maps the settlement relationship through algebraic encoding conversion; S5. Merge multiple instantaneous transient coefficient matrices to construct a global feature tensor, use an orthogonal model for dimensionality reduction projection, and output the polynomial evaluation point parameters. S6. Substitute the polynomial evaluation point parameters into the settlement higher-order polynomial for calculation to construct the polynomial commitment value, generate a zero-knowledge proof, and assemble the polynomial commitment value, the zero-knowledge proof, and the accumulated total transaction volume into a polynomial commitment aggregated transaction instruction and send it to the blockchain mainnet. S7. Obtain the multinomial commitment aggregation transaction instruction through the system consensus node and trigger the smart contract. Call the oracle to update the impedance parameters of the virtual distribution network model by combining the accumulated total electricity in the multinomial commitment aggregation transaction instruction, and replicate the benchmark transient reference space. S8. Extract zero-knowledge proofs from the parsed polynomial commitment aggregation transaction instructions, determine whether the evaluation point submitted as public input parameters along with the zero-knowledge proof falls within the benchmark transient reference space, and execute settlement after verification.

[0024] In one embodiment of the present invention, step S1 includes the following steps: Read the block header information at the start of the current settlement time window, and extract the block header random number and global state root as global block state data; The hash mapping algorithm is called to combine the random number in the block header and the global state root to calculate the initial sweep parameters, which include the center frequency and bandwidth parameters. Based on the structural topology characteristics of the local power distribution network, the initial frequency sweep parameters are adjusted by phase offset to generate a physical frequency sweep parameter set containing the center frequency, bandwidth, and target phase offset.

[0025] This process is executed by a regional aggregator deployed on the local power distribution network. The regional aggregator has a built-in communication interface for remote procedure call interaction with the blockchain mainnet and a driver unit for controlling the analog signal hardware. At the beginning of a settlement time window, the regional aggregator initiates a request through its communication interface to one or more consensus nodes of the target blockchain mainnet to obtain the latest block header information generated by the network.

[0026] Upon successful acquisition, the regional aggregator parses and extracts two key on-chain state variables from the latest block header information data structure: the block header random number, which serves as the proof-of-work result, and the global state root, which serves as the Merkle tree root hash of the account state. The regional aggregator then uses a pre-defined deterministic mapping algorithm to calculate and transform these extracted variables.

[0027] Specifically, the algorithm first concatenates the block header random number and the global state root in a predetermined byte order to form a combined input byte string. Then, it uses the secure hash algorithm SHA-256 to process the combined input byte string, generating a fixed-length hash digest. The region aggregator selects different byte segments from the hash digest and maps them to a preset physical parameter range through a linear scaling transformation, thereby calculating the initial frequency sweep parameters. For example, the center frequency is calculated using the following formula. With bandwidth :

[0028]

[0029] In the formula, Represents a specific center frequency. and These are the lower and upper limits of the preset sweep center frequency, respectively. Represents signal bandwidth. and These are the preset lower and upper bandwidth limits, respectively. This is the hash digest generated using the SHA-256 algorithm. (Function) Indicates from hash digest The Starting from the first byte, extract Each byte is interpreted as an unsigned integer. and This refers to the starting byte position and length selected for frequency calculation. and This refers to the starting byte position and length selected for bandwidth calculation.

[0030] After obtaining the initial frequency sweep parameters, to ensure that the subsequently injected frequency sweep signals can specifically cover the target network area or achieve the required signal-to-noise ratio, the regional aggregator further retrieves the structural topology features of the local distribution network under its jurisdiction from the local configuration library. This feature data is stored in the form of a weighted directed graph, representing the length, impedance, and number of downstream connected terminals of each line branch. Based on the structural topology features, the regional aggregator applies a phase offset adjustment operation to the initial frequency sweep parameters. This operation calculates the target phase offset index using a function related to the network's physical path.

[0031]

[0032] In the formula, The calculated target phase offset index. This represents the total number of major line branches included in the calculation within the local power distribution network. For the... Branches, It is its physical length. The dimensionless weight is set according to the importance or load of the branch. It is a phase adjustment coefficient, measured in rad / m, used to convert the weighted total path length into the corresponding phase shift. (Symbol) Indicates to Modulus taking to ensure phase value falls within Within the range.

[0033] The regional aggregator integrates the calculated specific center frequency, bandwidth, and target phase offset parameters to generate a structured set of physical sweep parameters, and caches this set locally in preparation for driving subsequent signal injection hardware.

[0034] It should be noted that the settlement time window is a preset time period based on the electricity market trading rules, such as every 15 minutes or every 5 minutes. The block header random number and global state root are fields directly obtained from blockchain block headers that conform to mainstream industry standards, ensuring unpredictability and network-wide consensus characteristics.

[0035] The deterministic mapping algorithm SHA-256 was chosen because of its excellent collision resistance and avalanche effect, ensuring that even small changes in the on-chain state can cause significant differences in the physical sweep parameters, thus preventing replay attacks of the physical excitation signal. The center frequency range... According to the relevant standards for power line carrier communication, if the application scenario is a low-voltage distribution network, a typical range is... kHz, this range is designed to avoid strong interference from power frequency and its low-order harmonics, while ensuring sufficient signal penetration. Bandwidth range A typical value is The kHz setting needs to balance data transmission rate and noise immunity. The structural topology characteristics are input and updated by the grid operator during system initialization or network structure changes, ensuring the accuracy of phase adjustment.

[0036] Phase adjustment coefficient The value of is determined based on prior network simulation or calibration tests on the actual network to compensate for the phase delay that occurs when the signal propagates on a typical line.

[0037] For example, at the start of a certain settlement time window, the latest block header information obtained by the regional aggregator contains a random number in the block header. The hexadecimal value is 0x1a2b3c4d, representing the global state root. The value is 0x...e5f6. The region aggregator concatenates the two and calculates the SHA-256 hash digest. The value is 0x2f8c...9a0b. The system default parameters are: kHz, kHz, kHz, kHz. To calculate the frequency, the system is set from... Take 4 bytes starting from byte 0 ( ), i.e., 0x2f8c..., whose corresponding integer value is 797723904. To calculate bandwidth, two bytes are taken starting from the 4th byte ( Its integer value is 39435. Therefore, the specific center frequency... The calculated result is approximately 124.2 kHz. Bandwidth The calculated frequency is approximately 32.1 kHz. Next, the regional aggregator retrieves the local distribution network topology, which contains two main branches with parameters as follows: m, ; m, The set phase adjustment coefficient rad / m. Then the target phase offset index. rad. Finally, the physical sweep parameter set generated by the regional aggregator is {center frequency: 124.2kHz, bandwidth: 32.1kHz, phase offset: 1.98rad}.

[0038] In one embodiment of the present invention, step S2 includes the following steps: High-frequency analog sweep pulses corresponding to the physical sweep frequency parameter set are generated through digital-to-analog conversion processing. The power drive unit of the control area aggregator couples high-frequency analog sweep pulses to the distribution network to generate high-frequency excitation signals; The high-frequency excitation signal is reflected by the impedance of the distribution network line to generate the power grid resonant response signal.

[0039] After step S1 is completed, the area aggregator immediately invokes the physical sweep parameter set to trigger its internally integrated analog signal generation hardware. This process is initiated by a digital signal processor (DSP), which first determines the specific center frequency defined in the physical sweep parameter set. ,bandwidth and target phase offset index A discrete-time high-frequency swept pulse digital sequence is synthesized. Preferably, the pulse is a linear frequency modulated signal, and its instantaneous frequency is within a preset pulse duration. From the inside Linear scan to The generation process of this numerical sequence is described by the following formula:

[0040] In the formula, For discrete time points The generated digital signal sample values. This is the preset amplitude of the signal. The duration of the sweep pulse. Represents discrete sampling time points, and its value range is... Other variables such as , , The definition has been given in the preceding steps.

[0041] The DSP sends the generated digital sequence to a high-speed digital-to-analog converter (DAC), which outputs a continuous-time analog voltage signal. This signal then passes through a low-pass reconstruction filter to eliminate high-frequency aliasing, generating a high-frequency analog sweep pulse that is time-synchronized with the current state of the blockchain network.

[0042] Next, the regional aggregator activates its equipped hardware takeover drive unit, which is essentially a module containing a power amplifier stage and signal coupling circuitry. The high-frequency analog sweep pulse is first boosted to a predetermined injection power level by the power amplifier stage, and then safely superimposed onto the distribution lines it manages through signal coupling circuitry, such as a capacitive coupling network, without affecting normal 50Hz power transmission. The high-frequency analog sweep pulse then propagates along the local distribution network lines to distant locations. During propagation, partial reflection occurs when the pulse encounters points of line impedance discontinuity, such as joints, branches, and various electrical loads, including target power terminals. All these reflected signals from various impedance elements at the distant end of the distribution line are superimposed and propagated back along the line, ultimately forming a composite signal containing the grid topology and the internal physical characteristics of each element—the grid resonant response signal. This signal is captured by corresponding monitoring equipment near the injection point for subsequent analysis.

[0043] The digital-to-analog converter (DAC) stream is a standard signal processing link that ensures accurate conversion from the digital domain to the physical analog domain. The pulse duration of the high-frequency analog sweep pulse. This is a key parameter, preferably between 1-10ms, representing a trade-off between signal energy and temporal resolution. The design of the hardware takeover drive unit must adhere to power line communication standards, ensuring that the injected signal power level is sufficient for clear reception at the remote end without causing electromagnetic interference to other equipment on the power grid. The power grid resonant response signal is a complex time-domain waveform; its shape is a snapshot of all physical characteristics along the frequency sweep path, serving as the raw data basis for subsequent physical attribute extraction steps. Signal amplitude Typically, the preferred range is 0.5-5V. The specific value is determined through experimental calibration based on the line attenuation characteristics and the ambient noise level to obtain the target signal-to-noise ratio.

[0044] For example, the regional aggregator obtains a set of physical frequency sweep parameters, where a specific center frequency... kHz, bandwidth kHz, and target phase offset parameters rad. The system calls internal configuration to set the signal amplitude for this frequency sweep. V, pulse duration The DSP synthesizes a digital linear frequency modulated (LFM) signal sequence based on this. The signal starts at a frequency of 108.15 kHz and linearly increases to a terminal frequency of 140.25 kHz within 5 ms, with an initial phase of 1.98 rad. This digital sequence is then processed by a 16-bit digital-to-analog converter with a sampling rate of 1 MHz to generate a high-frequency analog sweep pulse with a peak value of 1.5V. Subsequently, the power amplifier of the hardware-controlled drive unit amplifies this signal and couples it to a 220V power distribution line through a high-voltage isolation capacitor. The pulse propagates along the line, exciting multiple power terminals and loads along the way. The grid resonance response signal formed by the superposition of their reflected signals is recorded by the acquisition front end of the regional aggregator, forming a time-series voltage data containing thousands of sampling points, ready to be transferred to step S3 for processing.

[0045] In one embodiment of the present invention, step S3 includes the following steps: Capture the grid resonance response signal and line reflection noise within a preset timing window; Discrete wavelet transform is used to process the power grid resonance response signal and line reflection clutter, and the detail coefficients under the ultra-high frequency subband are extracted to generate the damped oscillation energy envelope distribution. Spatial feature vectors are extracted based on the energy envelope distribution of the damped oscillations, and then the spatial feature vectors are converted into a multi-dimensional array to construct the instantaneous transient coefficient matrix.

[0046] After the grid resonant response signal propagates along the distribution line and reaches the designated power terminal, the embedded processing system, based on the synchronization command received from the regional aggregator, activates its integrated synchronization sampling control chip within a preset timing cutoff window. The synchronization sampling control chip acquires the voltage or current waveforms on the local electrical circuit at a sampling rate much higher than the Nyquist frequency of the swept frequency signal, thereby completely capturing the mixed time-domain signal containing the grid resonant response signal, as well as line reflection clutter composed of inherent line reflections and environmental noise, generating a discrete digital signal sequence. .

[0047] To separate key transient components that characterize the physical properties of a terminal from complex mixed time-domain signals, the embedded processing system targets the acquired digital signal sequence. Discrete wavelet transform processing is performed. This process uses a pre-selected mother wavelet, such as the Daubechies4 wavelet, to decompose the digital signal sequence into multiple levels, projecting it onto a series of wavelet coefficients with different time-frequency resolutions. Based on prior knowledge or experimental calibration, the system selects detail coefficients falling within a preset ultra-high frequency sub-band. The frequency range of this sub-band is designed to maximize the ratio of reflected signal energy to background noise.

[0048] By processing the selected detail coefficients, such as calculating their squares and applying a smoothing filter window, the system can extract the time-varying decaying oscillation energy envelope distribution of the signal energy within the high-frequency subband:

[0049] In the formula, Representative at the The first decomposition level The integral energy of the signal within each time window together constitutes the decaying oscillation energy envelope distribution. It is the first A sequence of detail coefficients at each level. It is the sampling time interval, which is the reciprocal of the sampling rate. It is the number of samples contained in the smoothing window used to calculate local energy. It is the step size of the window sliding.

[0050] Based on the acquired decaying oscillation energy envelope distribution, the embedded processing system further calculates a series of statistical and morphological characteristic indicators to quantify the uniqueness of the energy envelope, thereby constructing a high-dimensional mathematical space feature vector. These characteristic indicators may include, but are not limited to, the total energy of the energy envelope, peak energy, peak occurrence time, energy decay time constant, kurtosis, and skewness.

[0051] Finally, to form a structured and fixed-dimensional data object, the embedded processing system fills the components of the high-dimensional mathematical space feature vector into a two-dimensional array in a predefined order, and uses the high-dimensional mathematical space feature vector to establish an instantaneous transient coefficient matrix characterizing the unique electromagnetic properties of the specified power terminal under this frequency sweep excitation. This matrix is ​​then packaged and sent to the regional aggregator as a physical credibility certificate of the terminal within the settlement time window.

[0052] It should be noted that the synchronous sampling control chip is a system-on-a-chip that integrates a high-speed analog-to-digital converter (ADC) and a precision clock synchronization circuit. Its typical sampling rate is 1-10 MSps to ensure sufficient resolution for high-frequency transient phenomena. The duration of the timing truncation window... Set to be slightly longer than the duration of the high-frequency analog sweep pulse. The typical value is 10-20ms to fully capture the excitation and decay process of the signal.

[0053] Discrete wavelet transform is a suitable time-frequency analysis tool for analyzing transient and non-stationary signals. Its selection aims to effectively separate reflected signals carrying characteristic information from low-frequency noise and power frequency interference. The preset ultra-high frequency sub-band range, preferably 250-500kHz, is the optimal frequency band determined based on offline analysis and simulation of the electromagnetic characteristics of a specific power distribution network environment. The dimension of the eigenvectors in the high-dimensional mathematical space... It is usually set to 16 or 36 so that it can be reshaped into or The matrix structure facilitates subsequent matrix operations and pattern recognition. The uniqueness of the instantaneous transient coefficient matrix stems from the unique internal circuit impedance, connecting cable length, and end load characteristics of each terminal. These minute physical differences can lead to distinguishable effects on the reflection and absorption characteristics of the swept frequency signal.

[0054] For example, the designated power terminal initiates the sampling process after its local clock synchronizes with the regional aggregator. With a timing truncation window of 15ms and a sampling rate of 2MSps, a digital signal sequence with a length of 30,000 sampling points is acquired. The terminal processor uses the db4 mother wavelet pair. A 6-level decomposition was performed. Analysis revealed that the detail coefficients at level 2... The signal-to-noise ratio of the reflected signal is optimal within the corresponding 250-500kHz frequency band; therefore, this band is selected as the preset ultra-high frequency sub-band. System application window length. Step length The energy envelope distribution of the damped oscillation was calculated. Subsequently, from Extracting 16-dimensional high-dimensional mathematical space feature vectors Its components may be: Total energy 5.4 × 10 -4 Peak energy 3.1×10 -5 The vector has 16 values, including peak time 6.2ms, decay constant 2.5ms, kurtosis 3.8, and skewness 0.7. Reshaped into The matrix is ​​used to generate the instantaneous transient coefficient matrix. For example, the first action The second line And so on. The matrix, serving as the physical identity verification for the terminal during this settlement cycle, is transmitted to the regional aggregator.

[0055] In one embodiment of the present invention, step S4 includes the following steps: Obtain valid electricity metering records of power terminals within the settlement period and identify the terminal identity identifier associated with the valid electricity metering records; The valid electricity metering records associated with each terminal's identity are mapped proportionally to polynomial coefficients. Based on the coefficients of each polynomial, perform algebraic recombination multiplication to generate a higher-order polynomial representing the total global electricity generation and consumption.

[0056] Centralized execution by regional aggregators is used to transform discrete energy consumption data from various power terminals within a complete billing cycle into a unified algebraic object. First, the regional aggregator gathers all data from its jurisdiction in its local storage. The electricity metering records submitted by a designated power terminal within the recently concluded single assessment window. Each electricity metering record is bound to the real terminal identity through the metadata of its data packet. The regional aggregator parses all data packets, verifies their digital signatures to confirm the authenticity of the source, and constructs a list containing... A temporary data table for each entry, where each entry maps a real terminal identity to its corresponding electricity metering record value.

[0057] The regional aggregator converts energy consumption data into algebraic elements, and then maps the electricity metering records corresponding to each real terminal identifier to a preset algebraic encoding function. This function will... Electricity metering records of each terminal Multiply by a fixed scaling factor This is mapped proportionally to an integer, and this integer constitutes the first integer in the fixed algebraic equation model. Each independent step term constant factor, also known as the polynomial coefficients. This is achieved by applying all... This mapping is performed on the electricity metering records of each terminal, and the regional aggregator generates a record containing... A series of tamper-proof polynomial coefficients with elements.

[0058] The region aggregator uses a series of tamper-proof polynomial coefficients to perform progressive algebraic recombination multiplication operations to construct the final aggregate object. Specifically, it will perform the following: The coefficients are used as part of the polynomial. The coefficients of the terms are determined, and all terms are summed to generate a higher-order polynomial that can comprehensively represent the global electricity generation and consumption data within the settlement period.

[0059] This polynomial compresses the energy consumption information of all terminals into a single structured mathematical object for subsequent dimensionality reduction verification and on-chain commitments. In the formula, This is the generated higher-order polynomial for settlement. This represents the total number of terminals involved in this aggregated settlement. For the terminal index, the value starts from... arrive . It is the first The electricity metering records reported by each terminal, in kWh. It is a dimensionless scaling factor used to convert electrical values ​​that may contain decimals into integers for calculations within a finite field. It is the independent variable of the polynomial.

[0060] It should be noted that the energy load data generated within a complete settlement cycle refers to the total electricity consumption accumulated and recorded by the metering chip of the power terminal during the standard electricity billing period. The true identity of the terminal is usually the public key hash of the terminal device or a decentralized identity identifier (DID), which is registered in the system when the terminal joins the network.

[0061] The process of mapping electricity metering records to tamper-proof polynomial coefficients is constructive and deterministic, ensuring that the same set of energy consumption data always produces the same polynomial. The tamper-proof property of these coefficients does not originate from themselves, but rather is a system-level security attribute obtained through subsequent combination with a polynomial commitment scheme. Scaling factor The setting is based on the required computational precision and the size of the underlying finite field, with a typical value being... This converts energy values ​​in kWh to integer values ​​in Wh. The order of the higher-order polynomial is then calculated. ,in This refers to the number of terminals participating in this settlement.

[0062] For example, at the end of a settlement cycle, the regional aggregator aggregates 4 ( Energy consumption data from power terminals. The parsed mapping relationship is as follows: the real terminal identifier is... The terminal, its electricity metering record kWh; correspond kWh; correspond kWh; correspond kWh. System scaling factor. Wh / kWh. The regional aggregator first converts each electricity record into polynomial coefficients. Calculated coefficients .for Calculated coefficients .for Calculated coefficients .for Calculated coefficients After obtaining a series of tamper-proof polynomial coefficients [2500, 1800, 3100, 900], the region aggregator performs an algebraic recombination operation to generate a higher-order settlement polynomial. Its specific form is This cubic polynomial It is cached as an algebraic credential representing the current global energy consumption status, awaiting further processing.

[0063] In one embodiment of the present invention, step S5 includes the following steps: Obtain the instantaneous transient coefficient matrices reported by each power terminal, and stack them according to the network topology order to synthesize a global feature tensor; Principal component analysis is used to perform dimensionality reduction projection on the global feature tensor, and the output is a feature fusion scalar that indicates the spatiotemporal coupling status of physical interactions. Perform boundary normalization operations on the feature fusion scalar to generate polynomial evaluation point parameters.

[0064] After generating the higher-order polynomial for settlement, the regional aggregator derives verification parameters that are strongly bound to the physical state of the power grid. First, the regional aggregator accumulates and indexes in its memory all instantaneous transient coefficient matrices belonging to the current settlement period, reported and generated by each designated power terminal.

[0065] The regional aggregator loads a predefined spatial topology arrangement of physical nodes from its system configuration. This order is, for example, based on the physical connection order of terminals on the power grid feeder or the sorting of geographic information system coordinates, and then allocates all nodes according to this fixed order. The instantaneous transient coefficient matrices are stacked along the new dimension, thus merging into a global feature tensor. This tensor is mathematically... The third-order tensor of the system structurally encodes the collective physical response of the entire tested distribution network. To extract a single indicator representing the overall system state from this high-dimensional data, the regional aggregator introduces an orthogonal space dimensionality reduction projection model based on eigenvalue decomposition, specifically implemented as a principal component analysis (PCA) algorithm. This algorithm first expands the global feature tensor along the terminal dimension to form a... The data matrix, where each row corresponds to a flattened feature vector of a terminal.

[0066] By calculating the covariance matrix of the data matrix and performing eigenvalue decomposition, the system can obtain a set of orthogonal eigenvectors and their corresponding eigenvalues. The system selects the first principal component vector corresponding to the largest eigenvalue, which indicates the direction of the largest variance in the dataset, reflecting the most prevalent common change pattern in the physical responses of all terminals. By projecting the mean vector of all terminal feature vectors onto this first principal component vector, the system can compress the overall global feature tensor and output a feature fusion scalar indicating the overall physical-temporal coupling status of the underlying infrastructure. To ensure that this physical scalar can be used for algebraic operations, normalization is performed.

[0067] In the formula, This is the unique polynomial evaluation point parameter ultimately generated, which is mapped to a prime-number finite field. The inner element is an integer. It is a feature fusion scalar output by the dimension reduction projection model. and These are the preset lower and upper limits of normalization. This is a preset large integer precision amplification factor. The prime number of the underlying elliptic curve scalar field of the zero-knowledge proof system.

[0068] Finally, the region aggregator extracts the normalized feature fusion scalar and directly uses it as the mandatory substitution reference condition for subsequent polynomial verification, thereby generating unique polynomial evaluation point parameters that fit the analytical calculation of higher-order polynomials. .

[0069] The global feature tensor, with its fixed arrangement order, ensures the reproducibility of each aggregation calculation, avoiding inconsistencies in results caused by disordered terminal order. The orthogonal space dimensionality reduction projection model based on eigenvalue decomposition is a standard multivariate statistical analysis method. Its core function is to extract the most representative latent factors of the system's commonalities from the interconnected physical response data of multiple terminals. Feature fusion scalar. In a physical sense, it can be understood as the projection of the entire power grid's physical state onto an abstract health coordinate axis. Its numerical fluctuations indirectly reflect macroscopic phenomena that may exist in the power grid, such as common-mode disturbances, line aging, or large-scale synchronous load changes. The upper and lower limits of the normalization range... and It is predetermined based on long-term historical data statistics or simulation analysis of power grid models, and represents the expected fluctuation range of the characteristic scalar under normal operating conditions.

[0070] For example, following the aforementioned steps, the regional aggregator has generated a higher-order settlement polynomial. The instantaneous transient coefficient matrix from the four terminals was collected. , , , The system defines the spatial topology arrangement order of physical nodes as ( ), then these four The matrices are stacked into The overall global feature tensor. Subsequently, these four matrices are flattened into 16-dimensional vectors, forming... The data matrix. The region aggregator performs principal component analysis on this matrix to calculate the first principal component vector representing the most significant direction of change in the data. Simultaneously, the mean vector of four 16-dimensional vectors was calculated. The feature fusion scalar is obtained by calculating the dot product of the two. Example of calculation results: The system reads preset normalization parameters from the configuration library. , .Will Substitute the values ​​into the normalization formula for calculation, assuming a preset large integer precision amplification factor. And prime numbers in a finite field Large enough that, when substituted into the formula, the unique polynomial evaluation point parameter is obtained. This integer value, located within a finite field, is the unique verification point jointly determined by the actual physical state of all terminals in the current power grid. It will be legally used for the cryptographic commitment and verification of the next step in the zero-knowledge proof system.

[0071] In one embodiment of the present invention, step S6 includes the following steps: Substitute the polynomial evaluation point parameters into the settlement higher-order polynomial to perform scalar function evaluation, and generate polynomial commitment values ​​based on the quotient polynomial. The zero-knowledge proof compiler is used to perform a validity calculation on the polynomial commitment value and output the zero-knowledge proof. The polynomial commitment value, zero-knowledge proof, polynomial evaluation point parameters (which are public input parameters), and the total accumulated transaction volume are serialized and packaged to generate a polynomial commitment aggregation transaction instruction.

[0072] After deriving the polynomial evaluation point parameters, the zone aggregator immediately initiates the cryptographic commitment generation and transaction assembly process for the settlement of higher-order polynomials. The zone aggregator then uses the polynomial evaluation point parameters derived in the previous steps... Substitute the corresponding higher-order polynomial for the calculation In the middle, scalar function evaluation is performed:

[0073] In the formula, It is a calculation of higher-order polynomials Parameters at the polynomial evaluation point The evaluation result value at the location. and This has been defined in the preceding steps.

[0074] Through this calculation, the region aggregator derives specific numerical results. This result is related to the polynomial itself, and also to the evaluation point tied to the physical state. Together, they constitute the core verification elements. Based on the Kate-Zaverucha-Goldberg polynomial commitment scheme, the regional aggregator uses merchant polynomials. Generate a proof, proving the polynomial At point The value is indeed The calculated polynomial commitment value is typically expressed as... .

[0075] To ensure the privacy and unforgeability of this computation process, the region aggregator invokes a zero-knowledge proof protocol compiler, such as Groth16 or PLONK. This compiler uses polynomials... Evaluation point and the evaluation result As input, based on the obtained polynomial commitment value Organizing data for the logical proof of the legality of mathematical calculations involves constructing complex mathematical proofs that demonstrate the validity of the polynomial evaluation relation and that the calculation process conforms to predetermined rules. Through this process, zero-knowledge proofs are produced that can prevent the transaction calculation process from being replayed or tampered with, denoted as... .

[0076] Finally, the regional aggregator will generate the new polynomial commitment value. Zero-knowledge proof Together with the polynomial evaluation point parameters, which are public input parameters The total transaction volume, along with the accumulated transaction volume used for final account reconciliation, is integrated into a standardized data structure using batch serialization packaging technology. This data structure is then created as a standardized multinomial commitment aggregation transaction instruction. This instruction is formatted as a transaction conforming to the target blockchain network protocol and broadcast into the blockchain mainnet's transaction pool via the communication interface of the regional aggregator, awaiting packaging and on-chain processing by consensus nodes.

[0077] Among them, the polynomial commitment value A point on the elliptic curve group is a core element of the KZG commitment scheme, representing the entire polynomial quotient and used for rapid verification. A zero-knowledge proof protocol compiler is a key software component for implementing zero-knowledge proof technology, transforming a computational statement into a proof that can be verified without revealing any additional information. Zero-knowledge proof. Its utility lies in the fact that on-chain smart contracts can be certain, simply by verifying this compact proof, that the submitter indeed possesses a valid polynomial. And correctly at the physically determined point The evaluation was performed without needing to know The specific coefficients. The total accumulated transaction volume is the sum of the electricity volumes of all terminals participating in this aggregate settlement, and is used as a publicly auditable value for final comparison with the intrinsic value of the multinomial commitment. Batch serialization packaging refers to organizing multiple heterogeneous data elements, such as elliptic curve points, binary proofs, and numerical values, into a single byte string according to an agreed format, so as to transmit it as a data carrier for a transaction.

[0078] For example, following the aforementioned steps, the regional aggregator holds and In a finite field Internal evaluation operation: Assume the result of the finite field operation is a specific integer. Subsequently, the regional aggregator utilized... The scheme calculates the polynomial with respect to the quotient. The commitment is used to obtain a polynomial commitment value. Next, it compiles this computational process into a zero-knowledge proof, producing a zero-knowledge proof. Simultaneously, the regional aggregator calculates the total accumulated transaction electricity, which is 8.3 kWh. Finally, it will... , The system packages the publicly available input parameters 59000000 and the value 8.3kWh, creates a multinomial commitment aggregation transaction instruction, and sends it to the blockchain mainnet.

[0079] In one embodiment of the present invention, step S7 includes the following steps: By listening to blockchain transactions to obtain multinomial commitment aggregation transaction instructions and triggering smart contracts, block records are queried in reverse order to reconstruct the physical sweep frequency parameter set; The oracle is invoked to load the physical sweep frequency parameter set, and the equivalent load impedance is calculated by combining the total electricity of the accumulated transaction in the polynomial commitment aggregation transaction instruction to update the virtual distribution network model. The theoretical electromagnetic wave propagation behavior is simulated in the updated virtual distribution network model. The set of impedance response distributions of the reference nodes received from the oracle is used to construct a reference transient reference space that characterizes normal physical energy consumption.

[0080] After a polynomial commitment aggregation transaction instruction is successfully recorded on the blockchain and added to a new block, one or more consensus nodes on the blockchain mainnet capture the transaction, identify its type, and activate the associated pre-deployed on-chain internal smart contract script. This triggers the execution of verification logic within the internal smart contract script. As the first step in its core verification process, the internal smart contract script performs a step-by-step inspection of the on-chain log, i.e., reverse-querying the historical state of the blockchain to locate the starting block that triggered this aggregation settlement, and reconstructing the actual physical frequency sweep parameter set used in step S1 from the block's header data in reverse order. This is achieved by replicating the deterministic mapping algorithm within the contract, using the same block header random number and global state root as input to recalculate the unique center frequency, bandwidth, and phase offset.

[0081] To establish an idealized physical benchmark, the internal smart contract's resident script calls one or more distributed oracle data interaction interfaces through its programming interface. These oracles are connected to one or more off-chain, high-performance computing servers. The smart contract uses the reconstructed physical sweep parameter set as a request parameter, entrusting the oracle array to read the accumulated total transaction amount from the transaction instruction, and based on this, calculates the currently declared equivalent load impedance of the system, dynamically updating the node impedance parameters in the pre-built virtual distribution network model scenario. Subsequently, the propagation and reflection behavior of the physical sweep parameter set in this updated model is simulated. The oracles use computationally efficient transmission line matrix models or high-frequency impedance network calculation algorithms to execute this simulation, quickly calculating the theoretical high-frequency response conduction behavior.

[0082] Finally, the oracle array aggregates and collects a set of baseline node impedance response distributions derived from the conduction behavior at various node positions in the virtual model. These distribution data are transmitted back to the smart contract, which uses the same dimension-reduced projection model as in step S5 to calculate and construct a theoretical evaluation point integer range that matches the physical state of the declared energy use condition, i.e., a baseline transient reference space.

[0083] An internal smart contract resident script is a piece of autonomous code deployed on the blockchain, whose execution is deterministic and guaranteed by network consensus. The ability to step through and examine the on-chain log is inherent to smart contracts because all historical states of the blockchain are publicly verifiable.

[0084] The reverse derivation process ensures that the physical incentive parameters used for on-chain verification are consistent with the parameters actually used off-chain. A distributed oracle array is a group of trusted off-chain entities that provide smart contracts with external data they cannot access themselves or perform complex calculations, reliably returning the results to the blockchain. The uninterrupted virtual power distribution network model is a digital twin built based on the engineering blueprint and standard component parameters of a real power grid, representing the electrical characteristics of the power grid under ideal conditions.

[0085] The simulation of theoretical electromagnetic wave propagation behavior is extremely complex and must therefore be performed by off-chain oracles; smart contracts themselves do not possess the capability to perform such calculations. The set of impedance response distributions of the benchmark nodes is simulated, representing ideal reflected signal data at various virtual terminal locations. The benchmark transient reference space may ultimately be defined as a single numerical range, such as [55000000, 65000000], which represents the legal range within which the polynomial evaluation point parameters, determined by the physical state, should fall under the condition of no anomalies.

[0086] For example, the multinomial commitment aggregation transaction instruction uploaded to the chain in the aforementioned steps is packaged into block #1001 by a consensus node. The associated smart contract is activated, which reads that the transaction was triggered by the state of block #1000. Therefore, the smart contract extracts the block header random number 0x1a2b3c4d and the global state root 0x...e5f6 from the block header of block #1000. Internally, it executes the same deterministic mapping algorithm as in step S1, recalculating the physical sweep frequency parameter set as {center frequency: 124.2kHz, bandwidth: 32.1kHz, phase offset: 1.98rad}.

[0087] Subsequently, the smart contract sends a request to the oracle array, sending this parameter set along with the ID of the target distribution network. An oracle node receives the request and loads the corresponding virtual distribution network model on its server. Based on the declared total electricity consumption of 8.3 kWh in the transaction, it estimates and injects the corresponding node load impedance network. Then, it uses a transmission line matrix model to simulate the propagation of a linear frequency modulated signal with an amplitude of 1.5V, a pulse width of 5ms, and a center frequency of 124.2kHz in the updated model. After the simulation, the oracle extracts the ideal reflected signal waveforms at the four virtual terminal locations, i.e., the set of baseline node impedance response distributions. These ideal waveforms undergo the same processing as steps S3 and S5: feature extraction, matrix construction, PCA dimensionality reduction, and finite field scaling and modulo mapping. Finally, it calculates the integer range of the theoretical evaluation point corresponding to this declared electricity load. The calculated range is [57000000, 61000000]. This integer interval is the baseline transient reference space, returned by the oracle to the smart contract for final verification in the next step.

[0088] In one embodiment of the present invention, step S8 includes the following steps: By parsing polynomial commitments and aggregating transaction instructions through smart contracts, the polynomial commitment value and zero-knowledge proof are extracted. Call the smart contract's built-in parser to determine whether the evaluation point submitted as a public input parameter along with the zero-knowledge proof falls within the baseline transient reference space; If the transaction is successful, the blockchain ledger module will be triggered, and the total transaction amount carried by the aggregated transaction instructions based on the polynomial commitment will be used to settle funds in the digital wallets of each end user.

[0089] After constructing the baseline transient reference space, the internal smart contract resident script enters the final verification and liquidation phase. This process is executed entirely within the secure entrusted environment of the internal smart contract resident script to ensure the integrity and immutability of the computation.

[0090] First, the smart contract parses and deserializes the ontology data of the polynomial commitment aggregation transaction instruction it is currently processing, extracting the key cryptographic variable generated and submitted off-chain by the regional aggregator from its data payload: the polynomial commitment value. Zero-knowledge proof And the evaluation point as a public input parameter .

[0091] Next, the core verification logic of the smart contract is triggered. It selects an asymmetric cryptographic parser adapted for homomorphic encryption comparison. This parser is essentially a pre-built verification function library within the contract, specifically designed to handle KZG commitments and zero-knowledge proofs. This function first utilizes the multinomial commitment value... This is used to verify the correctness of the evaluation relation. More importantly, it determines and verifies the evaluation points submitted as public input parameters along with zero-knowledge proofs. Whether it falls within the baseline transient reference space. That is, whether the evaluation point submitted by the contract verification is within the integer range of the baseline transient reference space generated in the previous step. Since the verification result is true, that is, the evaluation point completely falls within the target integer range, and both the polynomial commitment value and the zero-knowledge proof themselves have passed cryptographic verification, the entire transaction is determined to be legal and trustworthy. After the response captures the confirmation instruction code that the boundary between the judgment and verification is completely matched and the liquidation conditions are triggered, the smart contract executes its final financial operation part. It instructs the distributed layer ledger module, that is, the underlying ledger system of the blockchain, to allocate the corresponding total electricity fee to the on-chain escrow address controlled by the regional aggregator, according to the original electricity fee amount corresponding to the accumulated total electricity amount of the transaction as public information within the polynomial commitment aggregation transaction instruction.

[0092] Subsequently, the regional aggregator, based on its access to real energy consumption details, performs secondary allocation and final settlement of funds off-chain to the pre-bound digital wallet addresses of each electricity terminal owner. This process may be accomplished by calling the transfer function of a stablecoin contract compliant with the ERC-20 standard, thus completing the entire decentralized, physically verified electricity bill settlement loop.

[0093] In this context, a secure entrusted environment refers to the execution environment of a smart contract, where its code and state transitions are guaranteed by the consensus mechanism of the entire blockchain network, preventing external interference or tampering. An asymmetric cryptographic parser adapted for homomorphic encryption comparison refers to the set of functions in a smart contract used to verify KZG commitment pairing checks and zero-knowledge proofs. These functions verify cryptographic relationships through pairing operations on elliptic curves without decrypting any privacy data.

[0094] The valid evaluation domain is the baseline transient reference space calculated and returned by the oracle in step S7. Determining whether the evaluation point falls within this domain is the core of this invention, as it directly links the off-chain physical world state with the on-chain cryptographic verification logic.

[0095] The confirmation instruction code is the result of an if-then conditional statement in the smart contract. This code block is executed when all verifications pass. The distributed ledger module is essentially the state machine of the blockchain itself; smart contracts change account balances by calling its interface.

[0096] The initial electricity fee can be dynamically calculated by a smart contract based on the total electricity consumption accumulated from transactions and the current real-time electricity price in the market, or it can be calculated based on a pre-agreed fixed rate, where the real-time electricity price can be provided by another oracle.

[0097] For example, the smart contract has generated a baseline transient reference space [57000000, 61000000]. Processing of the multinomial commitment aggregation transaction instruction begins. The multinomial commitment value is extracted. Zero-knowledge proof And the publicly available input parameter is 59000000. First, it calls a finite-field asymmetric cryptographic verification function, which confirms... This is a valid and legitimate proof for the publicly evaluated point 59000000. Then, the core physical anti-counterfeiting cross-validation begins: contract checking. and Both conditions are met. At this point, all verifications are successful, triggering the rights confirmation logic. The smart contract reads the total accumulated electricity consumption carried in the transaction as 8.3 kWh. The contract queries the current electricity price as 0.15 stablecoins / kWh, and calculates the total electricity fee as follows: Stablecoins. Because the multinomial coefficients are private, the smart contract cannot directly distribute them based on labor. Therefore, it transfers these 1.245 stablecoins to an escrow address controlled by a regional aggregator for secondary distribution. The regional aggregator then distributes the stablecoins off-chain based on the raw energy consumption data it holds. kWh, proportionally to The digital wallets transferred 0.375, 0.27, 0.465 and 0.135 stablecoins respectively, finally completing the settlement and remittance of all funds.

[0098] See appendix Figure 2 This invention also proposes a blockchain-based power trading terminal data management and transaction settlement optimization system, comprising the following modules: The physical frequency sweep parameter generation module is used to obtain global block state data from the blockchain mainnet and generate a set of physical frequency sweep parameters for the local power distribution network through a deterministic mapping mechanism. The high-frequency excitation signal injection module is used to control the regional aggregator to inject high-frequency excitation signals into the associated distribution network topology circuit based on the physical frequency sweep parameter set, and to receive the grid resonance response signal reflected by the line impedance. The transient physical feature extraction module is used to control a designated power terminal to capture the grid resonance response signal and extract the instantaneous transient coefficient matrix that can characterize the electromagnetic features of the terminal using discrete wavelet transform. The settlement data multi-function construction module is used to summarize the electricity metering records reported by power terminals during the settlement period and generate a higher-order settlement polynomial that maps the settlement relationship through algebraic encoding conversion. The polynomial evaluation point derivation module is used to merge multiple instantaneous transient coefficient matrices to construct a global feature tensor, perform dimensionality reduction projection using an orthogonal model, and output the polynomial evaluation point parameters. The cryptographic commitment transaction assembly module is used to substitute the polynomial evaluation point parameters into the settlement higher-order polynomial for calculation to construct the polynomial commitment value, generate zero-knowledge proof, and assemble the polynomial commitment value, zero-knowledge proof and accumulated total transaction amount into a polynomial commitment aggregate transaction instruction and send it to the blockchain mainnet. The transient reference space construction module is used to obtain multinomial commitment aggregation transaction instructions through the system consensus node and trigger smart contracts, call oracles to update the impedance parameters of the virtual distribution network model by combining the accumulated total electricity in the multinomial commitment aggregation transaction instructions, and replicate the benchmark transient reference space. The on-chain verification and settlement execution module is used to parse the polynomial commitment aggregation transaction instruction to extract the zero-knowledge proof, determine whether the evaluation point submitted as a public input parameter along with the zero-knowledge proof falls within the benchmark transient reference space, and execute the settlement after the verification is successful.

[0099] Each of the modules can be implemented in whole or in part through software, hardware, or a combination thereof. It supports hardware embedded in or independent of the processor in the computer device, and also supports software stored in the memory of the computer device, so that the processor can call and execute the operations corresponding to each of the above modules.

[0100] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A blockchain-based method for optimizing power trading terminal data management and transaction settlement, characterized in that: Includes the following steps: S1. Obtain the global block state data of the blockchain mainnet and generate a physical frequency sweep parameter set for the local power distribution network through a deterministic mapping mechanism. Specifically, read the block header information at the start of the current settlement time window, extract the block header random number and global state root as global block state data; call the hash mapping algorithm to combine and process the block header random number and global state root, and calculate the initial frequency sweep parameters including the center frequency and bandwidth parameters; according to the structural topology characteristics of the local power distribution network, adjust the phase offset of the initial frequency sweep parameters to generate a physical frequency sweep parameter set including the center frequency, bandwidth and target phase offset. S2. The control area aggregator injects a high-frequency excitation signal into the associated distribution network topology circuit based on the physical frequency sweep parameter set, and receives the grid resonance response signal reflected by the line impedance. Specifically, it generates a high-frequency analog frequency sweep pulse corresponding to the physical frequency sweep parameter set through digital-to-analog conversion processing. The power drive unit of the control area aggregator couples the high-frequency analog frequency sweep pulse to the distribution network to generate a high-frequency excitation signal. The high-frequency excitation signal generates the grid resonance response signal through the reflection of the distribution network line impedance. S3. Control the designated power terminal to capture the grid resonance response signal, and use discrete wavelet transform to extract the instantaneous transient coefficient matrix that can characterize the electromagnetic characteristics of the terminal. S4. Summarize the electricity metering records reported by the power terminals during the settlement period, and generate a higher-order polynomial for the settlement that maps the settlement relationship through algebraic encoding conversion; S5. Merge multiple instantaneous transient coefficient matrices to construct a global feature tensor, use an orthogonal model for dimensionality reduction projection, and output the polynomial evaluation point parameters. S6. Substitute the polynomial evaluation point parameters into the settlement higher-order polynomial to construct the polynomial commitment value and generate a zero-knowledge proof; serialize and package the polynomial commitment value, the zero-knowledge proof, the polynomial evaluation point parameters as public input parameters, and the accumulated total transaction volume to generate a polynomial commitment aggregate transaction instruction and send it to the blockchain mainnet. S7. Obtain the multinomial commitment aggregation transaction instruction through the system consensus node and trigger the smart contract. Call the oracle to update the impedance parameters of the virtual distribution network model by combining the accumulated total electricity in the multinomial commitment aggregation transaction instruction, and replicate the benchmark transient reference space. S8. Extract zero-knowledge proofs from the parsed polynomial commitment aggregation transaction instructions, determine whether the evaluation point submitted as public input parameters along with the zero-knowledge proof falls within the benchmark transient reference space, and execute settlement after verification.

2. The method for optimizing data management and transaction settlement of power trading terminals based on blockchain according to claim 1, characterized in that, Step S3 includes the following steps: Capture the grid resonance response signal and line reflection noise within a preset timing window; Discrete wavelet transform is used to process the power grid resonance response signal and line reflection clutter, and the detail coefficients under the ultra-high frequency subband are extracted to generate the damped oscillation energy envelope distribution. Spatial feature vectors are extracted based on the energy envelope distribution of the damped oscillations, and then the spatial feature vectors are converted into a multi-dimensional array to construct the instantaneous transient coefficient matrix.

3. The method for optimizing data management and transaction settlement of power trading terminals based on blockchain according to claim 1, characterized in that, Step S4 includes the following steps: Obtain valid electricity metering records of power terminals within the settlement period and identify the terminal identity identifier associated with the valid electricity metering records; The valid electricity metering records associated with each terminal's identity are mapped proportionally to polynomial coefficients. Based on the coefficients of each polynomial, perform algebraic recombination multiplication to generate a higher-order polynomial representing the total global electricity generation and consumption.

4. The method for optimizing data management and transaction settlement of power trading terminals based on blockchain according to claim 1, characterized in that, Step S5 includes the following steps: Obtain the instantaneous transient coefficient matrices reported by each power terminal, and stack them according to the network topology order to synthesize a global feature tensor; Principal component analysis is used to perform dimensionality reduction projection on the global feature tensor, and the output is a feature fusion scalar that indicates the spatiotemporal coupling status of physical interactions. Perform boundary normalization operations on the feature fusion scalar to generate polynomial evaluation point parameters.

5. The method for optimizing data management and transaction settlement of power trading terminals based on blockchain according to claim 1, characterized in that, Step S6 includes the following steps: Substitute the polynomial evaluation point parameters into the settlement higher-order polynomial to perform scalar function evaluation, and generate polynomial commitment values ​​based on the quotient polynomial. The zero-knowledge proof compiler is used to perform a validity test on the polynomial commitment value and output the zero-knowledge proof.

6. The method for optimizing data management and transaction settlement of power trading terminals based on blockchain according to claim 1, characterized in that, Step S7 includes the following steps: By listening to blockchain transactions to obtain multinomial commitment aggregation transaction instructions and triggering smart contracts, block records are queried in reverse order to reconstruct the physical sweep frequency parameter set; The oracle is invoked to load the physical sweep frequency parameter set, and the equivalent load impedance is calculated by combining the total electricity of the accumulated transaction in the polynomial commitment aggregation transaction instruction to update the virtual distribution network model. The theoretical electromagnetic wave propagation behavior is simulated in the updated virtual distribution network model. The set of impedance response distributions of the reference nodes received from the oracle is used to construct a reference transient reference space that characterizes normal physical energy consumption.

7. The method for optimizing data management and transaction settlement of power trading terminals based on blockchain according to claim 1, characterized in that, Step S8 includes the following steps: By parsing polynomial commitments and aggregating transaction instructions through smart contracts, the polynomial commitment value and zero-knowledge proof are extracted. Call the smart contract's built-in parser to determine whether the evaluation point submitted as a public input parameter along with the zero-knowledge proof falls within the baseline transient reference space; If the transaction is successful, the blockchain ledger module will be triggered, and the total transaction amount carried by the aggregated transaction instructions based on the polynomial commitment will be used to settle funds in the digital wallets of each end user.

8. The method for optimizing data management and transaction settlement of power trading terminals based on blockchain according to claim 7, characterized in that, Settling funds to the digital wallets of various end users includes the following steps: The total electricity consumption is extracted from the aggregated transaction instructions of the multinomial commitment through smart contracts, and the total electricity cost is calculated by combining it with the real-time electricity price provided by the oracle. The total electricity cost will be allocated to an on-chain escrow address controlled by the regional aggregator. The regional aggregator initiates transfers to the digital wallets of each terminal user off-chain based on the electricity metering records of each terminal it holds, in proportion to complete the settlement.

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