Power grid state evaluation method and system based on large-capacity high-voltage active filter structure

Abstract

The present application relates to the field of power grid state evaluation, and discloses a power grid state evaluation method and system based on a large-capacity high-voltage active filter structure, which realizes breakthroughs in the fields of economic quantification of harmonic control, dynamic risk assessment, multi-party collaborative decision-making, data security and the like through interdisciplinary integration.The present application obtains multi-dimensional data of an active filter, processes and analyzes the data using quantum computing and other frontier technologies, generates results such as a quantum risk thermodynamic map, a device health degree attenuation curve and a Nash equilibrium decision path, and provides scientific decision support for power grid enterprises, users and regulatory agencies.Meanwhile, an anti-interference asset allocation table is encoded into a photon orbital angular momentum state, data topology protection is realized through an optical ring cavity, and a quantum anti-counterfeiting supervision report with anti-counterfeiting and traceability is generated.

Description

Technical Field

[0001] This invention relates to the field of power grid condition assessment, and in particular to a power grid condition assessment method and system based on a high-capacity high-voltage active filter structure. Background Technology

[0002] With the rapid development and increasing intelligence of power systems, power grid condition assessment technology has become crucial for ensuring the quality, security, and economy of power supply. High-capacity high-voltage active power filters (APFs), as important harmonic mitigation devices in power systems, directly impact the power quality and operational efficiency of the grid. However, traditional power grid condition assessment methods often focus on monitoring and analyzing single electrical parameters, making it difficult to comprehensively reflect the complex dynamic behavior and economic efficiency of APFs and the entire power grid.

[0003] Shortcomings of existing technology:

[0004] Traditional methods can only process limited electrical parameter data and are difficult to fully reflect the complex dynamic behavior of the power grid. For the performance evaluation of active filters, there is a lack of analysis methods that comprehensively consider multi-dimensional data such as harmonic suppression rate, equipment energy consumption, and maintenance costs.

[0005] Traditional methods are difficult to accurately assess the stability of equipment under complex operating conditions, cannot predict the risk of equipment failure in advance, and lack effective test signals and evaluation indicators to quantify the dynamic instability risk of equipment.

[0006] When making decisions, power grid companies, users, and regulatory agencies often lack comprehensive information and scientific decision support. Traditional methods are difficult to consider the strategic interactions of multiple stakeholders, which may lead to suboptimal decision outcomes.

[0007] Traditional regulatory reports are easily forged and altered, lack effective anti-counterfeiting measures, and the data and decision-making processes in regulatory reports lack traceability, making it difficult to conduct effective supervision and auditing.

[0008] To address the aforementioned shortcomings, this invention proposes a power grid condition assessment method and system based on a high-capacity high-voltage active filter structure. Summary of the Invention

[0009] This invention provides a power grid condition assessment method and system based on a high-capacity high-voltage active filter structure. Through interdisciplinary integration, it achieves breakthroughs in areas such as the economic quantification of harmonic control, dynamic risk assessment, multi-party collaborative decision-making, and data security.

[0010] The first aspect of this invention provides a power grid state assessment method based on a large-capacity high-voltage active power filter (HPV) structure. The method includes: acquiring the harmonic suppression rate, equipment energy consumption data, and maintenance cost records of the HPV; calculating the power quality loss cost per unit time for each harmonic frequency band; generating a harmonic economic impact factor matrix according to a time window; mapping the harmonic economic impact factor matrix to a quantum bit topology network; solving for the lowest energy state of the network using a quantum annealing processor; identifying high-energy nodes and outputting a quantum risk heatmap; injecting a chaotic test signal into the HPV; acquiring the voltage response signal at the output terminal; and calculating the Lyapunov coefficient of the voltage response signal. The exponential spectrum is used to select the largest positive exponent value as the equipment stability criterion, and the equipment health decay curve is output. Based on the quantum risk heat map and the equipment health decay curve, a three-party game tree of power grid companies, users, and regulatory agencies is constructed. The Nash equilibrium point is solved by reverse induction, and the Nash equilibrium decision path is output. The Nash equilibrium decision path is converted into the Hamiltonian of the spin glass system, and the system ground state is solved by simulated annealing algorithm, and the anti-interference asset allocation table is output. The anti-interference asset allocation table is encoded as photon orbital angular momentum state, and data topology protection is achieved through optical ring cavity to generate a quantum anti-counterfeiting supervision report. The quantum anti-counterfeiting supervision report includes the harmonic governance efficiency-cost ratio, dynamic electricity price strategy compliance index, and multi-party responsibility traceability chain.

[0011] Optionally, in the first implementation of the first aspect of the present invention, the method includes: collecting the harmonic suppression rates of each harmonic of the active power filter, real-time electricity price fluctuation data of the power grid node, equipment maintenance cost records, and historical life cycle data to obtain a timestamp-aligned raw data stream; calculating the sensitivity coefficient of real-time electricity price to harmonic disturbances according to the electricity market trading rules of the area where the power grid node is located to obtain a dynamic electricity price sensitivity coefficient table; constructing an equipment life attenuation model based on the cumulative running time and historical maintenance records of the active power filter to obtain an equipment life attenuation weight vector; inputting the raw data stream, sensitivity coefficient table, and attenuation weight vector into a matrix generator, verifying the integrity of the matrix data through an optical topology verifier, generating a timestamped digital fingerprint, and obtaining a verifiable harmonic economic impact factor matrix.

[0012] Optionally, in the second implementation of the first aspect of the present invention, the method includes: mapping the harmonic economic impact factor matrix to qubit nodes, converting the matrix element values ​​into coupling strength between qubits, defining the initial state of the qubit as the direction of the superconducting ring current, with clockwise representing low risk and counterclockwise representing high risk, to obtain a quantum coupling network topology; inputting the quantum coupling network topology into a quantum annealing processor, applying a transverse magnetic field and gradually reducing the magnetic field strength, causing the system to converge to the lowest energy state through the quantum tunneling effect, recording the final spin direction of each node, to obtain an annealed quantum state distribution table; extracting the energy values ​​from the annealed quantum state distribution table to obtain a three-dimensional risk level mapping table; encoding the three-dimensional risk level mapping table into a photon polarization state sequence, generating a holographic projection through a spatial light modulator, and representing the risk level with different color depths to obtain an interactive quantum risk heatmap.

[0013] Optionally, in the third implementation of the first aspect of the present invention, the method includes: generating a test signal with chaotic characteristics based on the Lorentz equations, injecting the test signal into the main circuit of the active filter through an isolation transformer to obtain a chaotic response dataset; reconstructing the phase space of the output signal in the chaotic response dataset, and calculating its maximum Lyapunov exponent λmax: when λmax>0, it is determined that the equipment has a risk of dynamic instability; the absolute value of the exponent is inversely proportional to the health of the equipment; obtaining a stability criterion table for the equipment; establishing a baseline health curve based on the equipment's historical maintenance records, and adopting a two-parameter exponential decay model: health score H(t)=H0×e^(-αt)+β×number of maintenance; where α is dynamically corrected by λmax, and β is determined by the quantified value of the maintenance effect; obtaining a dynamic decay model parameter set; inputting the dynamic decay model parameters into a failure time predictor, solving for the time point when the health score first falls below the safety threshold, and obtaining the equipment health decay curve.

[0014] Optionally, in the fourth implementation of the first aspect of the present invention, the method includes: extracting high-risk periods and frequency bands based on the quantum risk heatmap; generating equipment replacement budget allocation constraints for the power grid company; setting a time window for adjusting user electricity contracts based on key failure time nodes in the equipment health decay curve; retrieving the initial value of the dynamic compliance threshold from the power market regulatory rule base as the regulatory agency's strategy baseline; obtaining a three-party strategy space table; converting the risk level in the quantum risk heatmap into the economic loss weight of the power grid company; mapping the equipment health score to the cost sensitivity coefficient of user electricity contract adjustment; defining a positive correlation between the regulatory agency's compliance threshold adjustment step size and the risk level; and obtaining the game tree node parameter mapping. The utility function of each strategy combination is calculated layer by layer from the end node of the game tree: utility of the power grid company: reduction in equipment maintenance costs + improvement in power quality; utility of the user: reduction in electricity expenses - cost of adjusting electricity contracts; utility of the regulatory agency: improvement in compliance rate - audit costs; a set of candidate equilibrium strategies is obtained; solutions that meet the following conditions are selected from the set of candidate equilibrium strategies: equipment replacement cost of the power grid company ≤ budget constraint; adjustment range of user electricity contracts ∈ energy efficiency reward and punishment acceptance threshold; adjustment step size of regulatory compliance threshold matches risk level; a Pareto optimal solution set is obtained; according to the time window distribution of the quantum risk heatmap, the strategies in the Pareto optimal solution set are sorted by execution urgency to obtain the Nash equilibrium decision path.

[0015] Optionally, in the fifth implementation of the first aspect of the present invention, the method includes: mapping each strategy node in the Nash equilibrium decision path to a spin direction: spin-up represents executing the asset allocation decision, and spin-down represents postponing execution; defining the spin interaction strength according to the economic correlation between strategies to obtain a spin Hamiltonian parameter table; updating the spin direction at each temperature point through Monte Carlo sampling until the system energy converges to obtain a ground-state spin distribution map, marking the final direction and energy value of all spins; sorting the spin-up nodes in the ground-state spin distribution map according to the following rules: prioritizing the execution of strategies corresponding to high-risk periods and using the critical failure time in the equipment health decay curve as a hard deadline to obtain an equipment replacement sequence list; statistically analyzing the execution ratio of relevant strategies of power grid companies, users, and suppliers in the ground-state spin distribution, allocating the total maintenance cost proportionally to obtain a dynamic cost allocation ratio table; and matching the equipment replacement sequence list with the clauses in the power market regulatory rule base to obtain an anti-interference asset allocation table.

[0016] Optionally, in the sixth implementation of the first aspect of the present invention, the method includes: converting the anti-interference asset configuration table into a binary data stream; mapping each bit of data to a photon orbital angular momentum state using a spatial light modulator to obtain a topology-protected photon data stream; extracting the unit-time power quality loss cost from the harmonic economic impact factor matrix; and calculating the CBR value by combining it with the maintenance cost amortization ratio: CBR = total power quality improvement value / total maintenance cost; obtaining a dynamic CBR curve; and acquiring real-time electricity price data from the electricity market trading platform and comparing it with the theoretical electricity price in the Nash equilibrium decision path. Compliance Index = 1 - |Actual Electricity Price - Equilibrium Electricity Price| / Equilibrium Electricity Price; This yields the electricity price compliance index table; the execution records of the three-party game strategy are encoded as photon entangled state pairs, each subject is assigned a unique OAM entangled state identifier, and the strategy contribution weight is quantified by the correlation strength of the entangled photon pairs, resulting in an optical topology responsibility traceability chain; the topology-protected photon data stream, dynamic CBR curve, electricity price compliance index table, and optical topology responsibility traceability chain are input into the report synthesizer, and irreversible data binding is achieved through an optical ring cavity to generate a machine-readable regulatory report, resulting in a quantum anti-counterfeiting regulatory report.

[0017] The second aspect of this invention provides a power grid state assessment device based on a large-capacity high-voltage active filter structure. The device includes: an acquisition module for acquiring the harmonic suppression rate, equipment energy consumption data, and maintenance cost records of the large-capacity high-voltage active filter; calculating the power quality loss cost per unit time for each harmonic frequency band; and generating a harmonic economic impact factor matrix according to a time window; a processing module for mapping the harmonic economic impact factor matrix to a quantum bit topology network; solving for the lowest energy state of the network using a quantum annealing processor; and identifying high-energy nodes to output a quantum risk heatmap; and an assessment module for injecting chaotic test signals into the active filter, acquiring the voltage response signal at the output terminal, and calculating the Lyapunov coefficient of the voltage response signal. The system employs several modules: an exponential spectrum, a selection module for the largest positive exponential value as the equipment stability criterion, and an output equipment health decay curve; a decision module, which constructs a three-party game tree involving the power grid company, users, and regulatory agencies based on the quantum risk heatmap and the equipment health decay curve, solves for the Nash equilibrium point using backward induction, and outputs the Nash equilibrium decision path; a configuration module, which converts the Nash equilibrium decision path into the Hamiltonian of the spin glass system, solves for the system ground state using simulated annealing, and outputs an anti-interference asset allocation table; and a reporting module, which encodes the anti-interference asset allocation table into photon orbital angular momentum states, implements data topology protection through an optical ring cavity, and generates a quantum anti-counterfeiting regulatory report. This report includes a harmonic governance efficiency-cost ratio, a dynamic electricity price strategy compliance index, and a multi-party responsibility traceability chain.

[0018] A third aspect of the present invention provides a power grid condition assessment device based on a high-capacity high-voltage active filter structure, comprising: a memory and at least one processor, wherein the memory stores instructions; the at least one processor invokes the instructions in the memory to cause the power grid condition assessment device based on the high-capacity high-voltage active filter structure to execute the aforementioned power grid condition assessment method based on the high-capacity high-voltage active filter structure.

[0019] A fourth aspect of the present invention provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the above-described power grid state assessment method based on a high-capacity high-voltage active filter structure.

[0020] The technical solution provided by this invention has the following beneficial effects:

[0021] By using a harmonic economic impact factor matrix, the harmonic frequency band suppression rate is directly mapped to the power quality loss cost. Combined with a quantum annealing algorithm, high-risk periods (peak economic losses corresponding to higher harmonic frequency bands) are identified, allowing power grid companies to allocate maintenance budgets accordingly. When the economic loss weight of high-frequency harmonics is high, the corresponding filter modules are upgraded first.

[0022] Potential instability states of the equipment are induced by chaotic test signals (based on the Lorentz equations), and dynamic stability is quantified by combining the Lyapunov exponential spectrum. When the maximum positive exponent λmax > 0, the α parameter in the equipment health decay curve dynamically increases, triggering maintenance strategies in advance to avoid sudden failures.

[0023] The three-way game tree is mapped to the Hamiltonian of a spin glass system, and the ground state distribution is solved through Monte Carlo simulation annealing. When there is resource competition between the equipment replacement cost of power grid companies and the energy efficiency reward and punishment threshold of users, the strength of negative interaction drives strategic synergy (users accept short-term electricity price fluctuations in exchange for long-term power quality improvement).

[0024] An optical topology responsibility traceability chain is generated using photon orbital angular momentum (OAM) encoding, with each entity (power grid company, user) assigned a unique entangled state identifier. If a user improperly adjusts their electricity contract, the correlation strength of their entangled photon pairs abnormally decreases, allowing for rapid identification of the responsible party. Attached Figure Description

[0025] Figure 1 This is a schematic diagram of an embodiment of the power grid state assessment method based on a large-capacity high-voltage active filter structure in this invention.

[0026] Figure 2 This is a schematic diagram of another embodiment of the power grid state assessment method based on a large-capacity high-voltage active filter structure in this invention.

[0027] Figure 3 This is a schematic diagram of an embodiment of the power grid condition assessment device based on a large-capacity high-voltage active filter structure according to the present invention;

[0028] Figure 4 This is a schematic diagram of an embodiment of a power grid condition assessment device based on a high-capacity high-voltage active filter structure, as described in this invention. Detailed Implementation

[0029] This invention provides a power grid state assessment method and system based on a large-capacity high-voltage active filter structure. Through interdisciplinary integration, it achieves breakthroughs in areas such as the economic quantification of harmonic governance, dynamic risk assessment, multi-party collaborative decision-making, and data security.

[0030] For ease of understanding, the specific process of the embodiments of the present invention is described below. Please refer to [link / reference]. Figure 1 One embodiment of the power grid state assessment method based on a large-capacity high-voltage active filter structure in this invention includes:

[0031] 101. Real-time acquisition of harmonic suppression rate, equipment energy consumption data, and maintenance cost records for high-capacity high-voltage active filters; calculation of power quality loss cost per unit time for each harmonic frequency band (1-50th), using the formula: loss cost = harmonic amplitude × grid node electricity price sensitivity coefficient × equipment lifespan attenuation weight; generation of a harmonic economic impact factor matrix (HEIF matrix) according to time windows (1-minute granularity), where rows represent harmonic frequency bands, columns represent time windows, and element values ​​are the economic loss weight values ​​of the corresponding frequency band within the time window;

[0032] It is understood that the executing entity of this invention can be a power grid state assessment device based on a high-capacity high-voltage active filter structure, or it can be a terminal or a server; the specific implementation is not limited here. This embodiment of the invention will be described using a server as an example.

[0033] It should be noted that the data acquisition (10:00-10:05 UTC) includes: Harmonic suppression rate: Recording the suppression rate of the 1st to 50th harmonics via fiber optic current sensors (5th harmonic suppression rate 92%); Equipment energy consumption: Recording active power loss (Phase A 5.2kW, Phase B 5.0kW, Phase C 5.3kW); Maintenance cost: Retrieving the 3 most recent maintenance records from the maintenance database (filter module replacement cost $12,000 / time).

[0034] Loss cost calculation (taking the 10:01 minute window as an example):

[0035] Harmonic band Amplitude (A) Node price sensitivity coefficient Lifetime decay weight Cost of loss ($) 5 times 8.2 0.35 0.07 8.2×0.35×0.07=0.201 7 times 6.5 0.28 0.05 6.5×0.28×0.05=0.091 ... ... ... ... ... 23 times 3.1 0.18 0.12 3.1×0.18×0.12=0.067

[0036] HEIF matrix construction (time window: 2023-10-01T10:00 to 10:05), matrix dimensions: 50 rows (1st-50th harmonics) × 5 columns (time stamps per minute), matrix element values ​​are rounded to four decimal places, economic loss weight value = normalized (loss cost × 10^3), retaining three significant figures;

[0037] Among them, time synchronization: μs-level synchronous acquisition is achieved using IRIG-B time code; sensitivity coefficient: dynamically calculated based on IEEE1547 standard, ranging from [0.1, 0.5]; lifetime weight: device aging rate is calculated according to the Arrhenius model, with a temperature sampling interval of 30 seconds;

[0038] 102. Based on the harmonic economic impact factor matrix, a quantum bit topology network is mapped, where each quantum bit corresponds to a matrix element; the lowest energy state of the network is solved using a quantum annealing processor to identify high-energy nodes (i.e., high-risk periods and frequency bands for economic loss); a quantum risk heat map is output, labeled in three-dimensional coordinates: X-axis: harmonic frequency band (1-50th order), Y-axis: time window (UTC timestamp), Z-axis: risk level (1-10 levels, calculated by normalizing the quantum annealing energy value);

[0039] It should be noted that, taking the HEIF matrix (50×5 matrix) of a certain high-voltage active filter from 2025-03-21T10:00-10:05 as an example, only a portion of the data is selected:

[0040] Harmonic frequency band 10:00 Loss Weight 10:01 Loss Weight ... 10:04 Loss Weight 5 times 0.201 0.198 ... 0.205 7 times 0.091 0.089 ... 0.093 23 times 0.067 0.063 ... 0.068

[0041] Each matrix element maps to a topological qubit, where the loss weight value is converted into the spin direction of the qubit (a loss weight ≥ 0.2 maps to spin-up, otherwise spin-down). Using the topology of the Microsoft Majorana quantum chip, interaction edges are established between qubits in adjacent time windows and harmonic frequency bands. The weights are determined by the economic correlation between HEIF matrix elements (the correlation coefficient is 0.8 for adjacent time windows and 0.5 for adjacent frequency bands).

[0042] Quantum annealing was used to solve for the lowest energy state. A digital annealing algorithm was employed, with the following annealing parameters: initial temperature 1000K, cooling rate 0.95, and 1000 iterations. By solving for the lowest energy state of the Hamiltonian, high-energy nodes were identified: the 5th harmonic node at 10:02 had the highest energy value (0.215), corresponding to a risk level of 8; the 23rd harmonic node at 10:03 had the second highest energy value (0.185), corresponding to a risk level of 6.

[0043] Risk heatmap output, example of 3D coordinate data:

[0044] X-axis (harmonic frequencies) Y-axis (timestamp) Z-axis (risk level) 5 2025-03-21T10:02:00Z 8 23 2025-03-21T10:03:00Z 6

[0045] Normalization method: The quantum annealing energy value is scaled proportionally to levels 1-10 by the maximum value (0.25), with an energy difference of 0.025 corresponding to level 1 risk. The heatmap is rendered using WebGL, with a color gradient from green (low risk) to red (high risk), and supports clicking to query the economic loss weight of specific nodes and associated equipment parameters. Topology mapping: A King'sGraph network structure is used, with each quantum bit coupled to four adjacent nodes to simulate the propagation path of power grid harmonics. Annealing optimization: A simulated annealing algorithm based on a low-power annealing chip from Hunan University is used, with a single solution time of <50ms. Dynamic update: The HEIF matrix is ​​updated synchronously every 5 minutes, triggering annealing calculations to ensure real-time performance.

[0046] 103. Inject a chaotic test signal (Lorentz attractor waveform) into the active filter and acquire the voltage response signal at the output terminal; calculate the Lyapunov exponential spectrum of the response signal and select the maximum positive exponential value as the device stability criterion; output the device health decay curve, including: the current health score (based on Lyapunov exponential convergence), the performance prediction curve for the next 30 days (fitted according to the exponential decay model), and the key failure time node (the estimated time when the health falls below the safety threshold).

[0047] It should be noted that in a 10kV cascaded APF (three-phase Y-connection, 10 IGBT units per phase) in a steel plant, a Lorentz attractor waveform test signal was injected with the following parameters: chaotic parameters: σ=10, ρ=28, β=8 / 3; injected voltage amplitude: 5% of rated voltage (575V), duration 30 seconds.

[0048] Data Acquisition: A NIPXIe-5160 high-speed oscilloscope (sampling rate 2.5GS / s) was used to record the three-phase voltage waveforms at the APF output terminal, generating time series data. Figure 1 The voltage response waveform of phase A exhibits typical chaotic divergence characteristics.

[0049] Lyapunov exponent calculation involves reconstructing the phase space of the A-phase voltage response signal (embedding dimension m=3, time delay τ=5ms) and then using the Wolf algorithm to calculate the exponent spectrum.

[0050] Index direction Exponential value (bit / s) λ1 +0.82 λ2 -0.15 λ3 -2.37

[0051] The maximum positive exponent λ1 = 0.82 indicates that the system is sensitive to initial conditions and the device is in a metastable state.

[0052] Health decay curve generation, current score: based on λ1 convergence, score = 100 × (1 - λ1 / λmax), λmax = 1.5 (threshold according to IEC61000-4-30), score is 45.3 points (below the safety threshold of 60 points); 30-day prediction: the decay curve is fitted according to the exponential model: H(t) = H0 × e^(-kt), k = 0.023 / day (R² = 0.98), the score is predicted to drop to 30 points on day 25; critical failure node: when the score < 30, an early warning is triggered, the expected failure time is day 28 (confidence interval ± 1.5 days);

[0053] The testing was conducted during off-peak hours (23:00-02:00 UTC) to avoid production disruptions; the cooling system adopted a cascaded unit independent airflow design. Figure 3 (Structure) to ensure temperature rise ≤3℃ during testing; under abnormal operating conditions, SPLL technology is used to correct grid phase offset in real time to ensure data accuracy.

[0054] 104. Based on the quantum risk heatmap and the equipment health decay curve, construct a three-party game tree for the power grid company, users, and regulatory agencies; define the three-party strategy space: power grid company: equipment replacement budget allocation plan, maintenance cycle strategy; users: electricity contract adjustment range, energy efficiency reward and punishment acceptance threshold; regulatory agencies: dynamic compliance threshold setting rules, abnormal transaction inspection intensity; solve for the Nash equilibrium point through backward induction, and output the Nash equilibrium decision path, including: Pareto optimal solution set (balance point combination of technical parameters and economic indicators), and a list of priority execution of strategies for each subject;

[0055] It should be noted that the quantum risk heatmap selects high-risk nodes (5th harmonic risk level 8, corresponding to equipment maintenance cost of $12,000 / time) during the period of T10:02-10:03 on March 21, 2025; the health curve shows that the current equipment score is 45.3 points, and a failure warning is predicted to be triggered on day 25 (the maintenance cycle strategy must be completed within 25 days).

[0056] Game tree construction, defining the three-party strategy space and payoff function (unit: thousands of US dollars):

[0057] main body Strategy Options Parameter range Profit calculation formula (example) Power grid companies Budget allocation ratio α (new equipment / maintenance) α∈[0.3,0.7] Return = 10α - 5(1-α) - Risk Cost × 0.2 user Contract adjustment range β (±20%) β∈[-0.2,+0.2] Benefit = 50β2 + Energy Efficiency Reward / Penalty Threshold × 8 Regulatory agencies Compliance threshold γ (lower limit of risk level) γ∈[4,7] Revenue = Inspection intensity × 3 - Deviation penalty × 5

[0058] Solving using backward induction, at the third level of the game tree (regulatory decision-making): when γ=6, the audit intensity = 0.8, the deviation penalty = 0.4, and the maximum payoff is 12.4; backtracking to the second level (user decision-making): when β=+0.15, the user payoff is 6.3 and the constraint of γ=6 is satisfied; the final equilibrium point: the power grid company chooses α=0.55 (55% budget for new equipment), and the maintenance cycle is compressed to 20 days; the user accepts the contract adjustment of β=+0.15 (electricity price increase of 15%); the regulatory agency sets γ=6 (risk level ≥6 requires mandatory intervention);

[0059] Output results, Pareto optimal solution set: Technical parameters: Harmonic suppression rate ≥ 95%, maintenance cycle ≤ 20 days; Economic indicators: Total cost ≤ $15k, user electricity cost increase ≤ 18%;

[0060] Strategy Priority: Power grid companies will prioritize replacing 5th harmonic filter modules (equipment associated with high-risk nodes at 10:02); users will implement dynamic electricity price increases during the period from 10:00 to 11:00; regulatory agencies will initiate real-time audits of nodes with a risk level ≥6.

[0061] Among them, the risk cost weight = risk level × 1.5k / level (level 8 risk corresponds to 12k cost); the energy efficiency reward and punishment threshold is set according to the IEEE 2030.5 protocol, and the acceptance threshold is ±15%; the reverse induction method adopts a three-stage dynamic game model, and the calculation time is <200ms (based on the Gurobi optimizer).

[0062] 105. Based on the Nash equilibrium decision path, convert it into the Hamiltonian of the spin glass system, where: each spin represents an asset allocation decision (equipment replacement, budget allocation), and the spin interaction strength is determined by the economic correlation between decisions; use the simulated annealing algorithm to solve the system ground state and output an anti-interference asset allocation table, including: equipment replacement sequence (accurate to weekly granularity), maintenance cost sharing ratio (the proportion of the power grid company, user, and supplier), and regulatory compliance verification labels (compliant / to be corrected / non-compliant).

[0063] It should be noted that, taking the Nash equilibrium decision path of a regional power grid in Q2 2025 as an example, three key asset allocation decisions are selected: Spin 1 (S1): Equipment replacement sequence (0: delayed to week 12, 1: executed in week 8); Spin 2 (S2): Budget allocation ratio (0: 60% for the power grid company, 1: user-led allocation); Spin 3 (S3): Maintenance cycle (0: 20 days, 1: 15 days).

[0064] Constructing the Hamiltonian: ;

[0065] Among them: Economic correlation coefficient: J 12 =0.8 (Equipment replacement is strongly correlated with budget), J13 =0.5, J 23 =0.3; Local field coefficients: h1=1.2 (equipment delay cost), h2=0.9 (user budget sensitivity), h3=0.6 (compliance pressure);

[0066] Simulated annealing was used to solve the problem. The parameters were set as follows: initial temperature T0 = 1000, cooling rate α = 0.95, and number of iterations 1000. Candidate solutions were generated by generating neighborhood solutions through bit flipping (011 → 111).

[0067] Key data for the annealing process:

[0068] Number of iterations Temperature (K) Current energy Optimal Energy 0 1000 2.8 2.8 300 168 -1.2 -1.5 700 25 -2.1 -2.1 1000 7 -2.7 -2.7

[0069] Output the anti-interference configuration table, with the ground state solution being S=(1,0,1), corresponding to:

[0070] Configuration items Parameter value Economic Linkage Verification Equipment Replacement Sequence Start in week 8 (IGBT modules first) Compliant with GB / T14549 Cost sharing ratio Power grid 60%, users 25%, suppliers 15% To be corrected (user threshold 30%) Compliance Labels conform to Inspection intensity: 0.7

[0071] Among them, the interaction strength calculation is based on the marginal benefit difference of Pareto solutions in the game tree, and the Pearson correlation coefficient is used for quantification; annealing optimization is introduced by introducing an adaptive neighborhood search strategy. When there is no improvement after 50 consecutive iterations, the temperature is temporarily increased to 1.2Tcurrent to escape the local optimum; compliance verification is triggered by a correction warning for items with excessive amortization ratio.

[0072] 106. Encode the anti-interference asset allocation table as a photon orbital angular momentum state and achieve data topology protection through an optical ring cavity; generate a quantum anti-counterfeiting supervision report, including: Harmonic governance efficiency-cost ratio (CBR): the power quality improvement value per unit cost calculated based on the harmonic economic impact factor matrix; Dynamic electricity price strategy compliance index: comparing the deviation between real-time electricity price and Nash equilibrium solution; Multi-party responsibility traceability chain: recording the decision contribution weight of each subject through photon orbital angular momentum entanglement state;

[0073] It should be noted that, taking a certain power grid's Q3 2025 anti-interference asset allocation table (equipment replacement sequence: week 8; cost sharing ratio: power grid 60%, user 25%, supplier 15%) as an example:

[0074] The configuration table is converted into photonic orbital angular momentum states, with each configuration item corresponding to an OAM mode (topological charge number l = ±8, ±25, ±15). A dual topological protection is achieved using a topological defect lattice structure developed by the Nankai University team: momentum space protection: utilizing chiral symmetry to generate nontrivial entangled states, ensuring phase consistency of the OAM mode during transmission (phase error <0.1π); real space protection: constraining higher-order vortex modes through a toroidal photonic lattice (C8 symmetry), suppressing mode crosstalk (crosstalk rate <1e-4).

[0075] Data topology protection employs an optical ring cavity (5cm in diameter, Q value > 1e6) to store coded data, utilizing vortex coordination rotational symmetry to achieve: dynamic stability: OAM mode power fluctuation < 0.5dB when temperature fluctuates ±2℃; anti-interference: bit error rate < 1e-9 against electromagnetic interference (30dBm) and mechanical vibration (50Hz).

[0076] The quantum anti-counterfeiting report output, based on the HEIF matrix and Nash equilibrium solution from September 1st to September 7th, 2025, generates core indicators:

[0077] index Calculation method and numerical examples Harmonic mitigation effectiveness-cost ratio (CBR) Power quality improvement (ΔTHD=3.2%) / maintenance cost (15k) → CBR=213-1 Dynamic electricity price compliance index Real-time electricity price deviation (12.7%) normalized → index 62.3 (threshold > 60 compliant) Multi-party responsibility traceability chain Contribution weights of orbital angular momentum entanglement state records: power grid (58.7%), users (24.3%), suppliers (17.0%)

[0078] Among them, CBR calculation: based on the economic loss weight of the 5th harmonic in the HEIF matrix (0.215 / minute), combined with the suppression rate improvement after equipment replacement (92%→95%), the unit cost improvement value is quantified.

[0079] Photon entangled state: Bell state is constructed using OAM modes with l=8 and l=15, and the quantum immutability of the three-way weights is verified by coincidence counting measurement (CHSH inequality S=2.72>2); Topological verification label: A vortex filter is inserted at the output of the ring cavity, and the l=±25 mode is extracted to verify the compliance of the user sharing ratio (deviation from the preset value <1.5%).

[0080] In this invention, by collecting and processing data in real time, harmonic economic losses are accurately calculated, providing power grid companies with a basis for optimized decision-making. Quantum computing and risk identification technologies can quickly identify high-risk periods and frequency bands, helping to take timely suppression measures and reduce economic losses. Chaotic testing can assess equipment stability and predict equipment failure time, providing a scientific basis for maintenance plans. Equipment health decay curves can intuitively reflect the trend of equipment performance changes, facilitating timely maintenance measures. Three-party game trees and Nash equilibrium solving technologies can balance the interests of power grid companies, users, and regulatory agencies, achieving an optimal combination of technical parameters and economic indicators. Strategy priority lists can guide all parties to execute decisions in sequence, improving overall efficiency. Simulated annealing algorithms can solve the ground state of spin glass systems, outputting an anti-interference asset allocation table to ensure the economy and compliance of asset allocation. Maintenance cost sharing ratios and regulatory compliance verification labels can clarify the responsibilities of all parties and reduce disputes. Photon orbital angular momentum states and optical ring cavity technologies can achieve topological protection of data, improving data anti-interference and reliability. Quantum anti-counterfeiting regulatory reports can ensure the authenticity and immutability of data, providing strong support for supervision. In conclusion, by comprehensively utilizing various advanced technologies to conduct a thorough and detailed assessment and optimization of the power grid status, the economic efficiency, stability, and security of the power grid have been improved, providing a scientific basis for the decision-making of power grid companies.

[0081] Please see Figure 2 Another embodiment of the power grid state assessment method based on a large-capacity high-voltage active filter structure in this invention includes:

[0082] 201. Real-time acquisition of harmonic suppression rate, equipment energy consumption data, and maintenance cost records for each harmonic band (1-50th harmonic). Calculate the power quality loss cost per unit time for each harmonic band (1-50th harmonic). The formula is: Loss cost = Harmonic amplitude × Grid node electricity price sensitivity coefficient × Equipment lifespan attenuation weight. Generate a harmonic economic impact factor matrix (HEIF matrix) according to time windows (1-minute granularity). The matrix rows represent harmonic bands, the columns represent time windows, and the element values ​​are the economic loss weight values ​​of the corresponding frequency band within the time window.

[0083] Specifically, the system involves real-time fusion of multi-source heterogeneous data: Hardware sensors are used to collect the harmonic suppression rates of active power filters in real time, and real-time electricity price fluctuation data from power grid nodes is acquired simultaneously. Equipment maintenance cost records and lifecycle historical data are extracted from the enterprise resource planning system to obtain a timestamp-aligned raw data stream (containing a triplet sequence of harmonic amplitude, electricity price fluctuation value, and maintenance cost value). Dynamic electricity price sensitivity coefficient calibration: Based on the electricity market trading rules of the region where the power grid node is located, the sensitivity coefficient of real-time electricity price to harmonic disturbances is calculated. When the harmonic distortion rate exceeds a threshold, an electricity price penalty mechanism is triggered, and the sensitivity coefficient increases according to the penalty gradient. The sensitivity coefficient range is set to [0.1, 5.0], and the gradient table is determined through experimental calibration. Output product: a dynamic electricity price sensitivity coefficient table (containing the mapping relationship between timestamps, harmonic frequency bands, and sensitivity coefficients). Dynamic update of equipment lifespan degradation weight: based on the cumulative operating time of the active power filter. Based on historical maintenance records, a device lifespan degradation model is constructed: the basic degradation curve is defined as an exponential function, and the degradation rate is corrected by the maintenance interval; the degradation rate parameter is reset after each maintenance operation; output product: device lifespan degradation weight vector (a sequence of weight values ​​updated at a 1-minute granularity); HEIF matrix generation and verification: the original data stream, sensitivity coefficient table, and degradation weight vector are input into the matrix generator; matrix elements are filled according to the following rules: row index: harmonic frequency band (1-50); column index: time window (UTC time, 1-minute granularity); element value: the power quality loss cost caused by the corresponding harmonic frequency band within the current time window, calculated as: loss cost = harmonic amplitude × current sensitivity coefficient × current degradation weight; the integrity of the matrix data is verified through an optical topology verifier, generating a timestamped digital fingerprint; final product: a verifiable HEIF matrix, serving as the sole input source for generating a quantum risk heatmap.

[0084] It should be noted that the following is an example of real-time data acquisition and HEIF matrix generation for a high-capacity high-voltage active power filter from 10:00 to 10:05 (UTC) on March 20, 2025:

[0085] Real-time fusion of multi-source heterogeneous data, harmonic data acquisition: Real-time monitoring of the amplitude of the 1st to 50th harmonics using an embedded current sensor (accuracy ±0.5%). Specifically, at 10:00:00: the amplitude of the 5th harmonic is 0.3A, the 7th is 0.25A, and the 11th is 0.18A (other frequency bands are below the threshold and are ignored); at 10:01:00: the amplitude of the 5th is 0.35A and the 7th is 0.28A (due to sudden changes in grid load).

[0086] Electricity price data synchronization: Access the electricity market API to obtain real-time electricity price fluctuation data: The electricity price sensitivity coefficient is 1.8 from 10:00 to 10:05 (due to the current period being the peak electricity consumption period, the penalty gradient increases).

[0087] Maintenance cost extraction: Retrieve equipment life cycle data from the ERP system: Cumulative running time = 12,000 hours, last maintenance time = March 1, 2025, maintenance cost = 5,000 yuan / time.

[0088] Dynamic electricity price sensitivity coefficient calibration, penalty gradient rule:

[0089] Harmonic distortion rate (%) Sensitivity coefficient <5 0.1 5-10 1.8 >10 5.0

[0090] The 5th harmonic distortion rate at 10:01:00 is 6.2%, and the triggering electricity price sensitivity coefficient is 1.8.

[0091] The equipment lifespan degradation weight is dynamically updated. Degradation model parameters: Basic degradation formula: Weight = e^(-0.0001*t), reset to t=0 after maintenance. Current t=480 hours (since the last maintenance), weight=0.95.

[0092] Dynamic correction: Due to the sudden change in load at 10:01:00, the weight decay accelerated by 10%, and the corrected weight = 0.95 × 0.9 = 0.855.

[0093] HEIF matrix generation and validation, matrix filling example (partial data from 10:00:00 to 10:01:00):

[0094] Harmonic band Time window Harmonic amplitude Sensitivity coefficient Decay weight Cost of loss (RMB) 5 times 2025-03-20T10:00 0.3A 1.8 0.95 0.3×1.8×0.95=0.513 7 times 2025-03-20T10:00 0.25A 1.8 0.95 0.25×1.8×0.95=0.428 5 times 2025-03-20T10:01 0.35A 1.8 0.855 0.35×1.8×0.855=0.538

[0095] Data integrity verification: A digital fingerprint SHA-256 (a1b2c3...) is generated by comparing timestamp-aligned triple sequences (harmonics, electricity price, weights) using an optical topology verifier. If any data is missing or abnormal, an alarm is triggered and resampling is initiated.

[0096] Output product, HEIF matrix structure: Row index: 1st-50th harmonics (only effective frequency bands are displayed); Column index: 2025-03-20T10:00 to T10:05 (one column per minute);

[0097] Example element values: [5,T10:00] = 0.513 yuan; [5,T10:01] = 0.538 yuan;

[0098] The matrix quantifies the economic impact of different harmonic frequency bands, with the 5th harmonic causing the highest cost of loss (0.538 yuan) at 10:01, which should be prioritized for treatment.

[0099] 202. Based on the harmonic economic impact factor matrix, a quantum bit topology network is mapped, where each quantum bit corresponds to a matrix element; the lowest energy state of the network is solved using a quantum annealing processor to identify high-energy nodes (i.e., high-risk periods and frequency bands for economic loss); a quantum risk heat map is output, labeled in three-dimensional coordinates: X-axis: harmonic frequency band (1-50th order), Y-axis: time window (UTC timestamp), Z-axis: risk level (1-10 levels, calculated by normalizing the quantum annealing energy value);

[0100] Specifically, the quantum bit topology network is constructed as follows: HEIF matrix elements are mapped to quantum bit nodes, and matrix element values ​​are converted into coupling strengths between quantum bits. The initial state of a quantum bit is defined as the direction of the superconducting ring current, with clockwise representing low risk and counterclockwise representing high risk, resulting in a quantum coupling network topology diagram (including node connection weights and initial state distribution). Quantum annealing energy optimization involves inputting the quantum coupling network topology diagram into a quantum annealing processor, applying a transverse magnetic field and gradually decreasing its strength. Through quantum tunneling, the system converges to the lowest energy state. The final spin direction of each node is recorded, resulting in a annealed quantum state distribution table (including node numbers, ...). Spin direction and energy value); Risk level normalization mapping: Extract the energy values ​​from the annealed quantum state distribution table and convert them according to the following rules: the highest energy node (top 5%) is marked as level 10 risk; the energy value is divided into 10 levels according to percentile, with level 1 being the lowest risk; Output product: three-dimensional risk level mapping table; Optical topology visualization rendering: encode the three-dimensional risk level mapping table into a photon polarization state sequence; generate a holographic projection through a spatial light modulator, using different color depths to represent the risk level; Final product: interactive quantum risk heat map (supporting touch zoom and risk source tracing query), which serves as the sole input source for the multi-agent game decision-making model in step 4.

[0101] It should be noted that the following is an example of the conversion from the HEIF matrix (partial data) of a high-capacity high-voltage active filter to a quantum risk heatmap on March 20, 2025, from 10:00 to 10:05 (UTC):

[0102] Quantum bit topology network construction, HEIF matrix input: HEIF matrix dimensions: 50 rows (harmonic bands 1-50) × 5 columns (time window 10:00-10:05), element values ​​are economic loss weights (unit: yuan / minute). Example data:

[0103] Harmonic band 10:00 10:01 10:02 10:03 10:04 5 times 0.513 0.538 0.621 0.892 0.755 7 times 0.428 0.451 0.503 0.701 0.580 11 times 0.218 0.225 0.239 0.305 0.287

[0104] Quantum bit mapping rule: Each matrix element maps to one qubit, for a total of 250 qubits. The matrix element values ​​(economic loss weights) are converted into the coupling strength between qubits using the following formula:

[0105] ;

[0106] Example: The HEIF value of the 5th harmonic at 10:03 is 0.892, and the coupling strength with the adjacent time window (10:02) is: ;

[0107] Quantum state initialization: Define the initial ring current direction of the superconducting qubit as follows: clockwise (low risk): HEIF value < 0.5; counterclockwise (high risk): HEIF value ≥ 0.5; the initial state of the qubit with the 5th harmonic at 10:03 is counterclockwise (high risk). Quantum annealing energy optimization: Annealing parameters: initial transverse magnetic field strength: 100 mT, cooling rate: 1 mT per millisecond, annealing duration: 5 ms. Quantum tunneling effect: In the 10:03 time window, the 5th harmonic node triggers quantum tunneling due to high coupling strength (J = 0.0007565), and the spin direction flips from counterclockwise to clockwise (energy decrease).

[0108] Annealing results:

[0109] Quantum nodes (harmonic-time) Final spin direction Energy value (eV) 5 times - 10:03 counterclockwise 8.92 7 times - 10:03 counterclockwise 7.01 11 times - 10:03 clockwise 2.05

[0110] Risk level normalization mapping, energy value sorting: Highest energy node: 5th - 10:03 (8.92 eV, top 5%), marked as level 10 risk; Lowest energy node: 11th - 10:00 (2.05 eV, bottom 5%), marked as level 1 risk. Level division: Energy values ​​segmented by percentile:

[0111] percentile interval Risk level Color coding 95-100% Level 10 red 85-95% Level 9 orange color ... ... ... 0-5% Level 1 blue

[0112] Optical topology visualization rendering, photon polarization state encoding: Risk level (Z-axis) is mapped to photon polarization angle: θ = risk level / 10 × 180°; Level 10 risk corresponds to a polarization angle of 180°, and the photon polarization state is horizontal (red light). Holographic projection generation: 3D data is converted into a hologram using a spatial light modulator (SLM). The 5th harmonic is displayed as a red highlight area at 10:03, supporting touch zoom to view detailed parameters (economic loss weight, maintenance cost correlation). Output product, quantum risk heatmap: X-axis: 1st-50th harmonics (highlighting 5th and 7th harmonics); Y-axis: 2025-03-20T10:00 to T10:05; Z-axis: Risk level (5th harmonic - 10:03 marked as Level 10);

[0113] The heat map shows that the 5th harmonic is at the highest risk (red area) at 10:03, requiring priority adjustment of the filtering strategy or arrangement of equipment maintenance.

[0114] 203. Inject a chaotic test signal (Lorentz attractor waveform) into the active filter and acquire the voltage response signal at the output terminal; calculate the Lyapunov exponential spectrum of the response signal and select the maximum positive exponential value as the device stability criterion; output the device health decay curve, including: the current health score (based on Lyapunov exponential convergence), the performance prediction curve for the next 30 days (fitted according to the exponential decay model), and the key failure time node (the estimated time when the health falls below the safety threshold).

[0115] Specifically, the chaotic test signal generation and injection process involves generating a chaotic test signal based on the Lorentz equations. The waveform must satisfy the following conditions: non-periodicity and a spectrum covering the 1st to 50th harmonics; peak voltage of 10%-15% of the rated input voltage of the active filter; injection of the test signal into the main circuit of the active filter via an isolation transformer, with a duration ≤100ms; output product: a chaotic response dataset (containing the injected signal waveform and the output voltage response time sequence); Lyapunov exponent spectrum calculation: phase space reconstruction of the output signal in the chaotic response dataset is performed, and its maximum Lyapunov exponent λmax is calculated. When λmax > 0, the equipment is deemed to have a risk of dynamic instability; the absolute value of the exponent is inversely proportional to the equipment's health; output product: an equipment stability criterion table (including λmax value and convergence analysis results).

[0116] Health decay model construction: A baseline health curve is established based on the equipment's historical maintenance records, using a two-parameter exponential decay model: Health score H(t) = H0 × e^(-αt) + β × maintenance times; where α is dynamically corrected by λmax, and β is determined by the quantified maintenance effect; Output product: Dynamic decay model parameter set (including real-time updated values ​​of α and β and confidence intervals); Critical failure time prediction: The dynamic decay model parameters are input into the failure time predictor to solve for the time point when the health score first falls below the safety threshold: The safety threshold is retrieved from the IEEE Std18-2020 standard library according to the equipment model; The prediction time granularity is accurate to the hour; Final product: Equipment health decay curve (including the current score, the predicted curve for the next 30 days, and the critical failure node), which serves as the direct input to the multi-agent game decision model in step 4.

[0117] It should be noted that the following is an example of the chaos test and health assessment of a high-capacity high-voltage active power filter (model APF-10kV / 500A) at 10:00 on March 20, 2025:

[0118] Chaotic test signal generation and injection, Lorentz attractor waveform generation: Based on the Lorentz equations (parameters σ=10, ρ=28, β=8 / 3), a chaotic signal is generated with a spectrum covering the 1st to 50th harmonics, meeting the non-periodic requirement. Peak voltage = 12% × rated voltage (10kV) = 1.2kV, injected into the main circuit through an isolation transformer, with a duration of 80ms.

[0119] Output response acquisition: Acquire voltage signals at the injection point and the output terminal to generate a chaotic response dataset (time series length = 10,000 points, sampling rate = 1MHz).

[0120] Lyapunov exponent spectrum calculation and phase space reconstruction: The phase space is reconstructed using the delayed embedding method (delay time τ=5ms, embedding dimension m=3) to generate a three-dimensional trajectory matrix. Maximum Lyapunov exponent (λmax) calculation: The Wolf algorithm is used to calculate the output signal divergence rate, resulting in λmax=0.52 (>0, indicating a risk of dynamic instability in the device).

[0121] Health decay model construction, baseline health curve: Based on historical maintenance records (average maintenance interval of 180 days over the past 3 years), the initial health score H0 = 95 points (out of 100). Two-parameter exponential decay model: H(t) = 95 × e^(-0.0023t) + 0.15 × maintenance times (β is determined by the quantitative value of maintenance effect, and the maintenance effect score for this maintenance is 0.15). α dynamic correction: Since λmax = 0.52 > threshold 0.3, trigger α = base value × 1.2 = 0.00276. Critical failure time prediction, safety threshold setting: According to IEEE Std18-2020, the equipment health safety threshold = 60 points. Failure time calculation: Solving the equation 60 = 95 × e^(-0.00276t) + 0.15 × 3 (cumulative maintenance times), we get t ≈ 112 days, that is, the expected failure time = July 10, 2025 ± 6 hours.

[0122] Output product, equipment health decay curve: Current health: 82 points (2025-03-20T10:00).

[0123] Forecast values ​​for the next 30 days: 2025-04-01: 78 points; 2025-04-15: 71 points; 2025-04-30: 63 points; Critical failure point: 2025-07-10T14:30 (health score first drops below 60 points);

[0124] The curve indicates that the device's health will drop below the safety threshold within the next three months, and preventative maintenance is recommended before June 2025.

[0125] 204. Based on the quantum risk heatmap and the equipment health decay curve, construct a three-party game tree for the power grid company, users, and regulatory agencies; define the three-party strategy space: power grid company: equipment replacement budget allocation plan, maintenance cycle strategy; users: electricity contract adjustment range, energy efficiency reward and punishment acceptance threshold; regulatory agencies: dynamic compliance threshold setting rules, abnormal transaction inspection intensity; solve for the Nash equilibrium point through backward induction, and output the Nash equilibrium decision path, including: Pareto optimal solution set (balance point combination of technical parameters and economic indicators), and a list of priority execution of strategies for each subject;

[0126] Specifically, the tripartite strategy space is dynamically constructed as follows: High-risk periods and frequency bands are extracted based on the quantum risk heatmap from step 2, generating constraints for the power grid company's equipment replacement budget allocation; based on the key failure time nodes in the equipment health decay curve from step 3, a time window for adjusting user electricity contracts is set; the initial value of the dynamic compliance threshold is retrieved from the power market regulatory rule base as the regulatory agency's strategy baseline; output product: a tripartite strategy space table (containing the feasible strategy set and constraints for the power grid company, users, and regulatory agencies); Game tree physical parameter mapping: the risk level (Z-axis value) in the quantum risk heatmap is converted into the economic loss weight of the power grid company; the equipment health score is mapped to the cost sensitivity coefficient of user electricity contract adjustment; the positive correlation between the regulatory agency's compliance threshold adjustment step size and the risk level is defined; output product: a game tree node parameter mapping table (including conversion rules from technical parameters to economic indicators); inverse inductive equilibrium solution: tracing back from the end node of the game tree, the utility function value of each strategy combination is calculated layer by layer: power grid company... Utility: Reduced equipment maintenance costs + improved power quality benefits; User utility: Reduced electricity expenses - electricity contract adjustment costs; Regulatory utility: Improved compliance rate - audit costs; Output product: Candidate equilibrium strategy set (including utility values ​​of each node and strategy combinations); Pareto optimal solution selection: Select solutions from the candidate equilibrium strategy set that meet the following conditions: power grid company equipment replacement cost ≤ budget constraint; user electricity contract adjustment range ∈ energy efficiency reward and punishment acceptance threshold; regulatory compliance threshold adjustment step size matches risk level; Output product: Pareto optimal solution set (including techno-economic equilibrium point and tripartite utility values); Dynamic strategy priority ranking: Based on the time window distribution of the quantum risk heatmap, rank the strategies in the Pareto optimal solution set according to execution urgency: strategies corresponding to high-risk periods are marked for immediate execution; strategies corresponding to medium-risk periods are marked for execution this week; strategies corresponding to low-risk periods are marked for execution this month; Final product: Nash equilibrium decision path (including strategy execution list and priority labels), serving as the sole input source for spin glass theory optimization.

[0127] It should be noted that the following is an example of a game theory decision made by a power grid company based on the quantum risk heatmap and equipment health decay curve as of March 20, 2025:

[0128] Dynamic construction of the three-party strategy space. Input data: Quantum risk heatmap: High-risk period: 2025-03-20T10:03 (5th harmonic, risk level 10); Medium-risk period: 2025-03-20T10:01 (7th harmonic, risk level 7); Equipment health degradation curve: Critical failure time = 2025-07-10T14:30. Strategy constraints:

[0129] main body Strategy Space Constraints Power grid companies Equipment replacement budget allocation (Option A: Replace the 5th harmonic filter module, cost 1.2 million; Option B: Delay replacement) Budget cap 2 million user Electricity contract adjustment range (±10% load, acceptable threshold: adjustment cost ≤ 50,000 yuan / month) Time window: June 1, 2025 to July 10, 2025 Regulatory agencies Dynamic compliance threshold (harmonic distortion rate threshold adjusted from 5% to 4%, increasing inspection intensity by 20%). Adjusting the step size is positively correlated with the risk level.

[0130] Game tree physical parameter mapping, technical parameters → economic indicators:

[0131] Technical parameters Economic indicator mapping rules Example value Risk level (Z-axis value) Economic loss weight for power grid companies = risk level × 100,000 yuan / level Level 10 risk → 1 million yuan loss weight Equipment health score (H(t)=82) User cost sensitivity coefficient = 1 - H(t) / 100 Sensitivity coefficient = 1 - 82 / 100 = 0.18 Regulatory compliance adjustment steps Step size = Risk level × 0.2% (Level 10 risk → Adjustment 2%) The threshold was adjusted from 5% to 4% (an adjustment of 1%).

[0132] Back-inductive equilibrium solution, strategy combination and utility calculation (partial examples):

[0133] Strategy Combination Power grid enterprise utility (ten thousand yuan) User utility (ten thousand yuan) Regulator effectiveness (compliance rate improvement %) Option A + User Adjustment + Regulatory Threshold 4% -120+100=-20 8-4.5=3.5 15% - 5% (audit costs) = 10% Option B + No user adjustments + 5% regulatory threshold 0+0=0 0-0=0 5%-2%=3%

[0134] Candidate equilibrium strategy set: 1. {Option A, user adjustment ±10%, regulatory threshold 4%} → utility (-20, 3.5, 10); 2. {Option B, user adjustment ±5%, regulatory threshold 4.5%} → utility (0, 2.1, 8);

[0135] Pareto optimal solution selection and constraint verification:

[0136] Solution Number Power grid budget ≤ 2 million User adjustment cost ≤ 50,000 Regulatory steps match risk levels 1 1.2 million ≤ 2 million (√) 45,000 ≤ 50,000 (√) Adjust 1% to match level 10 risk (√) 2 0 ≤ 2 million (√) 21,000 ≤ 50,000 (√) Adjust by 0.5% to match level 7 risk (√)

[0137] Pareto optimal solution set: Solution 1: Techno-economic equilibrium point (replacing the 5th harmonic module, user adjustment ±10%, regulatory threshold 4%); Solution 2: Delayed replacement, user adjustment ±5%, regulatory threshold 4.5%;

[0138] Strategy priorities are dynamically sorted and executed based on urgency:

[0139] Strategy Associated risk period Execution priority Replace the 5th harmonic module High risk (10:03) Execute immediately (red) User adjusts load by ±10%. 30 days before critical failure This week's implementation (orange) Regulatory threshold 4% Long-term compliance requirements This month's implementation (blue)

[0140] Output Product: Nash Equilibrium Decision Path: 1. Immediate Execution: Power Grid Company: Replace 5 harmonic filter modules before March 25, 2025 (cost 1.2 million); Regulatory Agency: Implement a 4% harmonic distortion rate threshold starting March 21, 2025; 2. Execution This Week: Users: Sign electricity contract adjustment agreements before March 28, 2025 (±10% load, cost 45,000 / month); 3. Execution This Month: Regulatory Agency: Upgrade the abnormal transaction audit algorithm before April 10, 2025 (strength +20%).

[0141] This approach prioritizes high-risk harmonic issues, balancing equipment replacement costs (1.2 million ≤ budget 2 million), user acceptance (adjustment costs 45,000 ≤ 50,000) and regulatory compliance (threshold adjustment of 1% to match risk level), thus achieving Pareto optimality.

[0142] 205. Based on the Nash equilibrium decision path, convert it into the Hamiltonian of the spin glass system, where: each spin represents an asset allocation decision (equipment replacement, budget allocation), and the spin interaction strength is determined by the economic correlation between decisions; use the simulated annealing algorithm to solve the system ground state and output an anti-interference asset allocation table, including: equipment replacement sequence (accurate to weekly granularity), maintenance cost sharing ratio (the proportion of the power grid company, user, and supplier), and regulatory compliance verification labels (compliant / to be corrected / non-compliant).

[0143] Specifically, the construction of the Hamiltonian of the spin glass system involves mapping each strategy node in the Nash equilibrium decision path to a spin direction: spin-up represents executing the asset allocation decision (equipment replacement); spin-down represents postponing execution; the spin interaction strength is defined based on the economic correlation between strategies: if there is a cost synergy between the two strategies, the interaction strength is negative (promoting the same direction); if there is a resource competition conflict between the two strategies, the interaction strength is positive (promoting the opposite direction); the output product is a spin Hamiltonian parameter table (including spin direction definitions and interaction matrices); the simulated annealing ground state solution involves initializing the spin system temperature to 1000K and gradually cooling it to 1K using an exponential cooling strategy; updating the spin direction at each temperature point through Monte Carlo sampling until the system energy converges; the output product is a ground state spin distribution map (marking the final direction and energy value of all spins); asset allocation time series analysis involves sorting the spin-up nodes in the ground state spin distribution map according to the following rules: strategies corresponding to high-risk periods are executed first (from the Z-axis risk level of the quantum risk heatmap); the critical failure time in the equipment health decay curve is used as... The process involves several steps: 1) A hard deadline; 2) Output: Equipment replacement timeline (accurate to the weekly level, including execution priority tags); 3) Cost sharing ratio calculation: Calculating the execution ratios of relevant strategies for power grid companies, users, and suppliers in the ground-state spin distribution; 4) Allocating total maintenance costs proportionally, satisfying the following conditions: Power grid company sharing ratio ∈ [40%, 70%] (dynamically adjusted based on equipment ownership ratio); User sharing ratio ∈ [10%, 30%] (based on energy efficiency reward and penalty clauses in electricity contracts); Supplier sharing ratio ∈ [5%, 15%] (based on equipment warranty agreement terms); 5) Dynamic cost sharing ratio table (including the proportions of the three parties and the basis for adjustment); 6) Dynamic generation of compliance verification tags: Matching the equipment replacement timeline with clauses in the power market regulatory rule base: Compliant: All strategy execution times are earlier than the latest regulatory deadline; 7) Needs correction: At least one strategy execution time exceeds the deadline but does not reach the penalty threshold; 8) Violation: Key strategy execution time exceeds the regulatory penalty red line; 9) Final product: Anti-interference asset allocation table (including timeline, sharing ratio, and compliance tags), serving as the sole input source for generating the quantum anti-counterfeiting regulatory report.

[0144] It should be noted that the following is an example of spin glass system optimization by a power grid company based on the Nash equilibrium decision path on March 20, 2025:

[0145] Construction of the Hamiltonian of the spin glass system. Input data: Set of strategies in the Nash equilibrium decision path: Strategy 1: Replace the 5th harmonic filter module before 2025-03-25 (cost 1.2 million); Strategy 2: Adjust user load ±10% (cost 45,000 / month); Strategy 3: Regulatory threshold 4% (inspection cost +20%).

[0146] Spin mapping rule:

[0147] Policy Node Spin direction definition Economic Relationship Analysis Strategy 1 (Equipment Replacement) Spin Up (Execution) There is a synergistic effect with Strategy 3 (reducing compliance risk) → Interaction strength J = -0.5 Strategy 2 (User Adjustment) Spin Up (Execution) There is resource competition (budget conflict) with Strategy 1 → J = +0.3 Strategy 3 (Regulatory Adjustments) Spin down (temporarily suspended) Cooperative with strategy 1, independent of strategy 2 → J=0

[0148] Hamiltonian parameter table: Spin nodes: 1 (strategy 1), 2 (strategy 2), 3 (strategy 3);

[0149] Interaction matrix: J(1,2)=+0.3, J(1,3)=-0.5, J(2,3)=0;

[0150] The ground state solution is obtained by simulating annealing. Annealing parameters: initial temperature = 1000K, exponential cooling coefficient = 0.95, number of annealing iterations = 1000.

[0151] Monte Carlo sampling: At a temperature of T=10K, the system energy converges to the lowest state of -1.2eV.

[0152] Ground state spin distribution:

[0153] Spin Node Final direction Energy value (eV) 1 ↑ -0.8 2 ↓ -0.3 3 ↑ -0.1

[0154] Asset allocation timeline analysis and execution priority rules: Strategy 1 (related to high-risk periods) → immediate execution (before March 25, 2025); Strategy 3 (no hard deadline) → second priority execution (before April 10, 2025); Strategy 2 (user adjustment) due to downward spin → temporarily suspended.

[0155] Equipment replacement schedule:

[0156] Equipment type Execution time window Priority tags 5th harmonic filtering module 2025-03-25 (Week 12) Red (Immediately) Regulatory system upgrade 2025-04-10 (Week 15) Orange (this week)

[0157] Cost allocation ratio calculation, strategy execution ratio: Number of strategies executed for power grid companies: 2 (Strategy 1, Strategy 3); Number of strategies executed for users: 0; Number of strategies executed for suppliers: 0;

[0158] Application of cost-sharing rules:

[0159] main body Calculation of apportionment ratio Shared amount (total maintenance cost: 2 million) Power grid companies 70% (maximum ownership percentage) 1.4 million user 10% (lower limit of energy efficiency reward and penalty clauses) 200,000 supplier 15% (upper limit in the warranty agreement) 300,000

[0160] Compliance verification label generation and regulatory rule matching:

[0161] Strategy execution time Deadline for supervision Compliance assessment Replacement of 5th harmonic module 2025-04-01 Meets (√) Regulatory system upgrade 2025-04-30 Needs correction (Δ)

[0162] Output: Anti-interference asset configuration table:

[0163] 1. Equipment Replacement Schedule: 2025-03-25: Replacement of 5th harmonic filter module (cost 1.2 million); 2025-04-10: Monitoring system upgrade (cost 300,000);

[0164] 2. Maintenance cost sharing ratio: Power grid company: 1.4 million (70%); User: 200,000 (10%); Supplier: 300,000 (15%).

[0165] 3. Compliance verification label: 5th harmonic replacement: Compliant (√); Regulatory upgrade: Pending correction (Δ) (Implementation time is 10 days later than the standard deadline);

[0166] This configuration table excludes user load adjustment strategies (due to resource conflicts) through a spin glass model, prioritizes the replacement of high-risk equipment, and dynamically allocates costs (with the power grid company bearing 70%). It also marks items to be corrected for regulatory upgrades, providing structured input for quantum anti-counterfeiting reports.

[0167] 206. Encode the anti-interference asset allocation table as a photon orbital angular momentum state and achieve data topology protection through an optical ring cavity; generate a quantum anti-counterfeiting supervision report, including: Harmonic governance efficiency-cost ratio (CBR): the power quality improvement value per unit cost calculated based on the harmonic economic impact factor matrix; Dynamic electricity price strategy compliance index: comparing the deviation between real-time electricity price and Nash equilibrium solution; Multi-party responsibility traceability chain: recording the decision contribution weight of each subject through photon orbital angular momentum entanglement state;

[0168] Specifically, photonic orbital angular momentum encoding: The anti-interference asset configuration table from step 5 is converted into a binary data stream, and each bit of data is mapped to a photonic orbital angular momentum state (OAM state) through a spatial light modulator; OAM topology encoding rules are defined: the OAM order indicates the data field type (equipment replacement sequence is +5 order, cost amortization ratio is -3 order); the polarization direction indicates the data version identifier (left-handed is the current version, right-handed is the historical version); output product: topology-protected photonic data stream (containing dual encoding information of OAM state and polarization state); harmonic mitigation effectiveness-cost ratio (CBR) calculation: the unit time power quality loss cost is extracted from the harmonic economic impact factor matrix (HEIF matrix); combined with the maintenance cost amortization ratio, the CBR value is calculated: CBR = total power quality improvement / dimension Total cost of protection; Output product: Dynamic CBR curve (efficiency-cost ratio sequence updated weekly); Generation of dynamic electricity price compliance index: real-time electricity price data is obtained from the electricity market trading platform and compared with the theoretical electricity price in the Nash equilibrium decision path; Calculation of deviation index: compliance index = 1 - |actual electricity price - equilibrium electricity price| / equilibrium electricity price; Output product: electricity price compliance index table (including timestamp, deviation degree and risk level label); Construction of multi-party responsibility traceability chain: the execution record of the three-party game strategy is encoded as photon entangled state pairs: each subject (power grid company, user, regulatory agency) is assigned a unique OAM entangled state identifier; the strategy contribution weight is quantified by the correlation strength of the entangled photon pairs; Output product: optical topology responsibility traceability chain (the decision contribution of each subject can be restored by interferometer measurement);

[0169] Quantum Anti-counterfeiting Report Synthesis and Verification: The topology-protected photonic data stream, dynamic CBR curve, electricity price compliance index table, and optical topology responsibility traceability chain are input into the report synthesizer; irreversible data binding is achieved through an optical ring cavity to generate a machine-readable regulatory report, including: tamper-proof feature: OAM topology code check bit; visualization module: risk heatmap overlaid with CBR curve; audit interface: supports regulatory agencies to retrieve original photonic data via API for topology verification; final product: quantum anti-counterfeiting regulatory report (outputting both optical media version and digital signature version).

[0170] It should be noted that the following is an example of a quantum anti-counterfeiting regulatory report generated by a power grid company based on its anti-interference asset allocation table as of March 25, 2025:

[0171] Photon orbital angular momentum (OAM) encoding, input data: Anti-interference asset configuration table (partial):

[0172] Equipment replacement sequence: Replaced the 5th harmonic module (APF-10kV / 500A) on March 25, 2025; Maintenance cost sharing ratio: Power grid company 70% (1.4 million), user 10% (200,000), supplier 15% (300,000); Compliance label: Equipment replacement complies (√), regulatory upgrade pending correction (Δ);

[0173] OAM encoding rules:

[0174] Data fields OAM order polarization direction Binary encoding example Equipment Replacement Sequence +5 Left-handed 11010011 (UTC timestamp) Cost sharing ratio -3 Left-handed 1010 (Power grid 70%) Compliance Labels +1 right-handed 01 (Compliant / To be corrected)

[0175] Output product: Topology-protected photon data stream (partial): Photon 1: OAM+5, left-handed polarization, encoded 11010011; Photon 2: OAM-3, left-handed polarization, encoded 1010; Photon 3: OAM+1, right-handed polarization, encoded 01.

[0176] Harmonic mitigation effectiveness-cost ratio (CBR) calculation, input data: HEIF matrix (March 20, 2025 to March 25, 2025): Total cost reduction of power quality loss = 5 million yuan; Total maintenance cost = 2 million yuan (grid 1.4 million yuan + users 200,000 yuan + suppliers 300,000 yuan + others 100,000 yuan).

[0177] CBR calculation: CBR = 2 million yuan / 5 million yuan = 2.5; meaning that for every 1 yuan invested in maintenance costs, the power quality benefits are improved by 2.5 yuan.

[0178] Output: Dynamic CBR curve (weekly granularity): 2025-03-25: CBR=2.5 (peak performance); 2025-03-18: CBR=1.8;

[0179] Dynamic electricity price compliance index generation. Input data: theoretical equilibrium electricity price (Nash equilibrium solution) = 0.6 yuan / kWh; actual real-time electricity price = 0.58 yuan / kWh (2025-03-25T10:00).

[0180] Deviation calculation: Deviation = ;

[0181] Compliance index = 1 − 0.0333 = 0.9667 (96.67%);

[0182] Output Product: Electricity Price Compliance Index Table:

[0183] Timestamp Deviation Compliance Index Risk level 2025-03-25T10:00 3.33% 96.67% Low risk

[0184] Construction of a multi-party responsibility traceability chain, and coding of policy execution records:

[0185] main body OAM entangled state identifier Strategy contribution weight (association strength) Power grid companies OAM+ 8th level 0.8 (Decision on Replacing Leading Equipment) user OAM+2nd level 0.5 (partially subject to load adjustment) Regulatory agencies OAM-5 0.6 (threshold adjustment contribution)

[0186] Interferometer measurement results: Three-party strategy correlation strength: power grid company → regulatory agency = 0.75, user → power grid company = 0.3.

[0187] Quantum anti-counterfeiting report synthesis and verification, data binding and output: tamper-proof features: optical ring cavity generating hash fingerprint, visualization module, audit interface;

[0188] Final product: Quantum Anti-counterfeiting Supervision Report (March 25, 2025): Optical medium version: burned onto quantum storage optical disc, OAM topology check bit matching rate 99.99%; Digital signature version: PDF file (digital digest: x509-SHA256);

[0189] This report utilizes photonic coding and quantum entanglement technologies to irreversibly link asset allocation, CBR efficiency, electricity price compliance, and accountability, providing regulatory agencies with verifiable and tamper-proof decision-making support. The contribution weight of power grid companies' equipment replacement strategies reaches 0.8, consistent with their 70% cost-sharing ratio, validating the reliability of the data topology.

[0190] In this embodiment of the invention, real-time data acquisition and processing ensure the accuracy and timeliness of the evaluation results. The application of quantum computing and chaos testing technologies further improves the accuracy and sensitivity of the evaluation. The output of quantum risk heatmaps and equipment health decay curves provides power grid companies with scientific decision-making basis. The optimized asset allocation scheme of Nash equilibrium decision path balances equipment replacement costs, user electricity costs, and regulatory compliance, improving the economy and reliability of the power grid. The application of three-party game tree and Nash equilibrium solving technology considers the interests of power grid companies, users, and regulatory agencies. Through the strategy execution priority list, it guides all parties to execute decisions in sequence, achieving a win-win situation for all parties. The application of photon orbital angular momentum state encoding and optical ring cavity technology realizes the topological protection and immutability of data. The quantum anti-counterfeiting regulatory report provides regulatory agencies with verifiable and tamper-proof decision-making basis, enhancing the transparency and credibility of regulation. The simulated annealing algorithm solves the ground state of the spin glass system, outputting the optimal asset allocation scheme. The dynamic cost sharing ratio table and regulatory compliance verification label ensure the rationality of resource allocation and the reduction of maintenance costs. In summary, this technology, by comprehensively utilizing a variety of advanced technical means, achieves a comprehensive and dynamic assessment and optimization of the power grid status, improves the economy, reliability and security of the power grid, provides a scientific basis for power grid companies' decision-making, and promotes a win-win situation for all stakeholders.

[0191] The above describes the power grid state assessment method based on a large-capacity high-voltage active filter structure in the embodiments of the present invention. The following describes the power grid state assessment device based on a large-capacity high-voltage active filter structure in the embodiments of the present invention. Please refer to [link / reference]. Figure 3An embodiment of the power grid state assessment device based on a large-capacity high-voltage active filter structure in this invention includes: an acquisition module 301, used to acquire the harmonic suppression rate, equipment energy consumption data, and maintenance cost records of the large-capacity high-voltage active filter, calculate the power quality loss cost per unit time for each harmonic frequency band, and generate a harmonic economic impact factor matrix according to a time window; a processing module 302, used to map the harmonic economic impact factor matrix into a quantum bit topology network, solve the minimum energy state of the network through a quantum annealing processor, identify high-energy nodes, and output a quantum risk heatmap; and an assessment module 303, used to inject chaotic test signals into the active filter, acquire the voltage response signal at the output end, calculate the Lyapunov exponent spectrum of the voltage response signal, and screen the largest positive exponent. The value serves as a criterion for equipment stability, outputting an equipment health decay curve. Decision module 304 constructs a three-way game tree among the power grid company, users, and regulatory agencies based on the quantum risk heatmap and the equipment health decay curve, solves for the Nash equilibrium point using reverse induction, and outputs the Nash equilibrium decision path. Configuration module 305 converts the Nash equilibrium decision path into the Hamiltonian of the spin glass system, solves for the system ground state using simulated annealing, and outputs an anti-interference asset allocation table. Report module 306 encodes the anti-interference asset allocation table into photon orbital angular momentum states, implements data topology protection through an optical ring cavity, and generates a quantum anti-counterfeiting regulatory report. The quantum anti-counterfeiting regulatory report includes the harmonic governance efficiency-cost ratio, dynamic electricity price strategy compliance index, and a multi-party responsibility traceability chain.

[0192] In this embodiment of the invention, by integrating multiple functional modules, a comprehensive and dynamic assessment and optimization of the power grid status is achieved, which improves the economy, stability and security of the power grid, provides a scientific basis for the decision-making of power grid companies, and promotes cooperation and win-win results among multiple stakeholders.

[0193] above Figure 3 The power grid condition assessment device based on a large-capacity high-voltage active filter structure in this embodiment of the invention is described in detail from the perspective of modular functional entities. The power grid condition assessment device based on a large-capacity high-voltage active filter structure in this embodiment of the invention is described in detail below from the perspective of hardware processing.

[0194] Figure 4This is a schematic diagram of a power grid state assessment device 400 based on a large-capacity high-voltage active filter structure, provided by an embodiment of the present invention. The power grid state assessment device 400 based on the large-capacity high-voltage active filter structure can vary significantly due to different configurations or performance. It may include one or more central processing units (CPUs) 410 (e.g., one or more processors) and a memory 420, and one or more storage media 430 (e.g., one or more mass storage devices) storing application programs 433 or data 432. The memory 420 and storage media 430 can be temporary or persistent storage. The program stored in the storage media 430 may include one or more modules (not shown in the diagram), each module may include a series of instruction operations on the power grid state assessment device 400 based on the large-capacity high-voltage active filter structure. Furthermore, the processor 410 may be configured to communicate with the storage media 430 and execute the series of instruction operations in the storage media 430 on the power grid state assessment device 400 based on the large-capacity high-voltage active filter structure.

[0195] The power grid condition assessment device 400 based on a high-capacity high-voltage active filter structure may also include one or more power supplies 440, one or more wired or wireless network interfaces 450, one or more input / output interfaces 460, and / or one or more operating systems 431, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD, etc. Those skilled in the art will understand that... Figure 4 The illustrated power grid condition assessment device structure based on a high-capacity high-voltage active filter structure does not constitute a limitation on the power grid condition assessment device based on a high-capacity high-voltage active filter structure. It may include more or fewer components than illustrated, or combine certain components, or have different component arrangements.

[0196] The present invention also provides a power grid condition assessment device based on a high-capacity high-voltage active filter structure. The power grid condition assessment device based on a high-capacity high-voltage active filter structure includes a memory and a processor. The memory stores computer-readable instructions. When the computer-readable instructions are executed by the processor, the processor performs the steps of the power grid condition assessment method based on a high-capacity high-voltage active filter structure described in the above embodiments.

[0197] The present invention also provides a computer-readable storage medium, which can be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium, wherein the computer-readable storage medium stores instructions that, when the instructions are executed on a computer, cause the computer to perform the steps of the power grid state assessment method based on a high-capacity high-voltage active filter structure.

[0198] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0199] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0200] The above-described 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.

Claims

1. A power grid state assessment method based on a high-capacity high-voltage active filter structure, characterized in that, The power grid state assessment method based on a large-capacity high-voltage active filter structure includes: Acquire the harmonic suppression rate, equipment energy consumption data and maintenance cost records of high-capacity high-voltage active filters, calculate the power quality loss cost per unit time for each harmonic frequency band, and generate a harmonic economic impact factor matrix according to the time window. Based on the harmonic economic impact factor matrix, a quantum bit topology network is mapped, and the minimum energy state of the network is solved by a quantum annealing processor to identify high-energy nodes and output a quantum risk heat map. A chaotic test signal is injected into the active filter, the voltage response signal at the output terminal is acquired, the Lyapunov exponent spectrum of the voltage response signal is calculated, the maximum positive exponent value is selected as the device stability criterion, and the device health degradation curve is output, including: A test signal with chaotic characteristics is generated based on the Lorentz equations. The test signal is then injected into the main circuit of the active filter through an isolation transformer to obtain a chaotic response dataset. The phase space of the output signal in the chaotic response dataset is reconstructed, and its maximum Lyapunov exponent λ_max is calculated: when λ_max > 0, the device is determined to have a risk of dynamic instability; the absolute value of the exponent is inversely proportional to the health of the device; and a device stability criterion table is obtained. A baseline health curve is established based on the equipment's historical maintenance records, using a two-parameter exponential decay model. Health score H(t) = H_0 × e^(-αt) + β × maintenance times; Among them, α is dynamically corrected by λ_max, and β is determined by the quantified value of the maintenance effect; Obtain the parameter set of the dynamic decay model; Input the parameters of the dynamic decay model into the failure time predictor to solve for the time point when the health score first falls below the safety threshold, and obtain the equipment health decay curve. Based on the quantum risk heatmap and the equipment health decay curve, a three-party game tree is constructed for the power grid company, users, and regulatory agencies. The Nash equilibrium point is solved by reverse induction, and the Nash equilibrium decision path is output. Based on the Nash equilibrium decision path, the Hamiltonian of the spin glass system is converted, and the ground state of the system is solved by the simulated annealing algorithm to output an anti-interference asset allocation table. The anti-interference asset allocation table is encoded as a photon orbital angular momentum state, and data topology protection is achieved through an optical ring cavity to generate a quantum anti-counterfeiting supervision report. The quantum anti-counterfeiting supervision report includes the harmonic governance efficiency-cost ratio, dynamic electricity price strategy compliance index, and multi-party responsibility traceability chain.

2. The power grid state assessment method based on a large-capacity high-voltage active filter structure according to claim 1, characterized in that, include: Collect the harmonic suppression rates of active filters, real-time electricity price fluctuation data of grid nodes, equipment maintenance cost records and life cycle historical data to obtain a timestamp-aligned raw data stream; Based on the electricity market trading rules of the region where the power grid node is located, the sensitivity coefficient of the real-time electricity price to harmonic disturbances is calculated, and a dynamic electricity price sensitivity coefficient table is obtained. Based on the cumulative runtime and historical maintenance records of the active filter, a device lifespan attenuation model is constructed to obtain the device lifespan attenuation weight vector. The original data stream, sensitivity coefficient table, and attenuation weight vector are input into the matrix generator. The integrity of the matrix data is verified by an optical topology verifier, and a timestamped digital fingerprint is generated to obtain a verifiable harmonic economic impact factor matrix.

3. The power grid state assessment method based on a large-capacity high-voltage active filter structure according to claim 1, characterized in that, include: The harmonic economic impact factor matrix is ​​mapped to qubit nodes, and the matrix element values ​​are converted into the coupling strength between qubits. The initial state of the qubit is defined as the direction of the superconducting ring current, with clockwise representing low risk and counterclockwise representing high risk, thus obtaining the topology of the quantum coupling network. The topology of the quantum coupled network is input into the quantum annealing processor. A transverse magnetic field is applied and the magnetic field strength is gradually reduced. The system is brought to converge to the lowest energy state through the quantum tunneling effect. The final spin direction of each node is recorded to obtain the quantum state distribution table after annealing. The energy values ​​in the quantum state distribution table after annealing are extracted to obtain a three-dimensional risk level mapping table; The three-dimensional risk level mapping table is encoded as a sequence of photon polarization states, and a holographic projection is generated through a spatial light modulator. The risk level is represented by different color depths, resulting in an interactive quantum risk heatmap.

4. The power grid state assessment method based on a large-capacity high-voltage active filter structure according to claim 1, characterized in that, include: Based on the quantum risk heat map, high-risk periods and frequency bands are extracted, and constraints on the allocation of equipment replacement budgets for power grid companies are generated. According to the key failure time nodes in the equipment health decay curve, the time window for adjusting user electricity contracts is set, and the initial value of the dynamic compliance threshold is retrieved from the power market regulatory rule base as the policy baseline for regulatory agencies. Obtain the three-party strategy space table; The risk levels in the quantum risk heatmap are converted into economic loss weights for power grid companies, and the equipment health score is mapped to the cost sensitivity coefficient of user electricity contract adjustments. The positive correlation between the regulatory agency's compliance threshold adjustment step size and the risk level is defined; thus, a game tree node parameter mapping table is obtained. Starting from the terminal node of the game tree, the utility function value of each strategy combination is calculated layer by layer: utility of the power grid company: reduction in equipment maintenance costs + improvement in power supply quality; utility of the user: reduction in electricity expenses - cost of adjusting electricity contracts; utility of the regulatory agency: improvement in compliance rate - audit costs; thus obtaining the set of candidate equilibrium strategies; From the candidate equilibrium strategy set, select solutions that meet the following conditions: the power grid company's equipment replacement cost ≤ budget constraint; the adjustment range of the user's electricity contract ∈ energy efficiency reward and penalty acceptance threshold; the adjustment step size of the regulatory compliance threshold matches the risk level; and obtain the Pareto optimal solution set. Based on the time window distribution of the quantum risk heatmap, the strategies in the Pareto optimal solution set are sorted according to their execution urgency to obtain the Nash equilibrium decision path.

5. The power grid state assessment method based on a large-capacity high-voltage active filter structure according to claim 1, characterized in that, include: Map each strategy node in the Nash equilibrium decision path to a spin direction: spin up represents executing the asset allocation decision, and spin down represents postponing execution. The spin interaction strength is defined based on the economic correlation between strategies, and the spin Hamiltonian parameter table is obtained. The spin direction is updated by Monte Carlo sampling at each temperature point until the system energy converges, resulting in a ground state spin distribution map, which marks the final direction and energy value of all spins. The nodes with spin-up in the ground state spin distribution diagram are sorted according to the following rules: the strategies corresponding to high-risk periods are executed first, and the key failure time in the equipment health decay curve is taken as the hard deadline, so as to obtain the equipment replacement sequence list. The execution ratios of relevant strategies of power grid companies, users, and suppliers in the ground state spin distribution are statistically analyzed, and the total maintenance cost is allocated proportionally to obtain a dynamic cost allocation ratio table. The equipment replacement schedule is matched with the clauses in the power market regulatory rules library to obtain the anti-interference asset configuration table.

6. The power grid state assessment method based on a large-capacity high-voltage active filter structure according to claim 1, characterized in that, include: The anti-interference asset configuration table is converted into a binary data stream, and each bit of data is mapped to a photonic orbital angular momentum state through a spatial light modulator to obtain a topology-protected photonic data stream. Extract the unit time power quality loss cost from the harmonic economic impact factor matrix, and calculate the CBR value by combining it with the maintenance cost amortization ratio: CBR = Total power quality improvement / Total maintenance cost; Obtain the dynamic CBR curve; Real-time electricity price data is obtained from the electricity market trading platform and compared with the theoretical electricity price in the Nash equilibrium decision path: Compliance Index = 1 - |Actual Electricity Price - Equilibrium Electricity Price| / Equilibrium Electricity Price; Obtain the electricity price compliance index table; The execution records of the three-party game strategy are encoded as photon entangled state pairs. Each agent is assigned a unique OAM entangled state identifier. The strategy contribution weight is quantified by the correlation strength of the entangled photon pairs, thus obtaining an optical topology responsibility traceability chain. The topology-protected photonic data stream, dynamic CBR curve, electricity price compliance index table, and optical topology responsibility traceability chain are input into the report synthesizer. Irreversible data binding is achieved through an optical ring cavity to generate a machine-readable regulatory report, resulting in a quantum anti-counterfeiting regulatory report.

7. A power grid condition assessment device based on a high-capacity high-voltage active filter structure, characterized in that, The method for assessing the state of a power grid based on a high-capacity high-voltage active filter structure as described in any one of claims 1 to 6 includes: The acquisition module is used to acquire the harmonic suppression rate, equipment energy consumption data and maintenance cost records of each harmonic active filter, calculate the power quality loss cost per unit time for each harmonic frequency band, and generate a harmonic economic impact factor matrix according to the time window. The processing module is used to map the harmonic economic impact factor matrix into a quantum bit topology network, solve the minimum energy state of the network through a quantum annealing processor, identify high-energy nodes, and output a quantum risk heat map. The evaluation module is used to inject chaotic test signals into the active filter, acquire the voltage response signal at the output terminal, calculate the Lyapunov exponent spectrum of the voltage response signal, select the maximum positive exponent value as the device stability criterion, and output the device health degradation curve, including: A test signal with chaotic characteristics is generated based on the Lorentz equations. The test signal is then injected into the main circuit of the active filter through an isolation transformer to obtain a chaotic response dataset. The phase space of the output signal in the chaotic response dataset is reconstructed, and its maximum Lyapunov exponent λ_max is calculated: when λ_max > 0, the device is determined to have a risk of dynamic instability; the absolute value of the exponent is inversely proportional to the health of the device; and a device stability criterion table is obtained. A baseline health curve is established based on the equipment's historical maintenance records, using a two-parameter exponential decay model. Health score H(t) = H_0 × e^(-αt) + β × maintenance times; Among them, α is dynamically corrected by λ_max, and β is determined by the quantified value of the maintenance effect; Obtain the parameter set of the dynamic decay model; Input the parameters of the dynamic decay model into the failure time predictor to solve for the time point when the health score first falls below the safety threshold, and obtain the equipment health decay curve. The decision-making module is used to construct a three-party game tree of power grid companies, users and regulatory agencies based on the quantum risk heat map and the equipment health decay curve, solve for the Nash equilibrium point through backward induction, and output the Nash equilibrium decision path. The configuration module is used to convert the Nash equilibrium decision path into the Hamiltonian of the spin glass system, solve the system ground state using the simulated annealing algorithm, and output an anti-interference asset configuration table. The reporting module encodes the anti-interference asset allocation table into photon orbital angular momentum states, achieves data topology protection through an optical ring cavity, and generates a quantum anti-counterfeiting regulatory report. The quantum anti-counterfeiting regulatory report includes the harmonic governance efficiency-cost ratio, dynamic electricity price strategy compliance index, and multi-party responsibility traceability chain.

8. A power grid condition assessment device based on a high-capacity high-voltage active filter structure, characterized in that, The power grid condition assessment device based on a high-capacity high-voltage active filter structure includes: a memory and at least one processor, wherein the memory stores instructions; The at least one processor invokes the instructions in the memory to cause the power grid condition assessment device based on the high-capacity high-voltage active filter structure to execute the power grid condition assessment method based on the high-capacity high-voltage active filter structure as described in any one of claims 1-6.

9. A computer-readable storage medium storing instructions thereon, characterized in that, When the instruction is executed by the processor, it implements the power grid state assessment method based on a large-capacity high-voltage active filter structure as described in any one of claims 1-6.

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