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

Abstract

The invention relates to the field of power grid state evaluation, discloses a power grid state evaluation method and system based on a high-capacity high-voltage active filtering structure, and realizes breakthrough in the fields of economic quantification of harmonic suppression, dynamic risk evaluation, multi-party collaborative decision, data security and the like through interdisciplinary fusion. According to the method, the multi-dimensional data of the active filter is obtained, processing and analysis are carried out by utilizing advanced technologies such as quantum calculation, results such as a quantum risk thermodynamic diagram, an equipment health degree attenuation curve and a Nash equilibrium decision path are generated, and scientific decision support is provided for power grid enterprises, users and supervision organizations. Meanwhile, an anti-interference asset configuration table is coded into a photon orbital angular momentum state, data topology protection is achieved through an optical annular cavity, and a quantum anti-counterfeiting supervision report with anti-counterfeiting performance and traceability is generated.

Description

Technical Field

[0001] The present invention relates to the field of power grid state assessment, and in particular to a power grid state assessment method and system based on a large-capacity high-voltage active filtering structure. Background Art

[0002] With the rapid development and increasing intelligence of power systems, grid status assessment technology has become critical to ensuring the quality, safety, and affordability of power supply. Large-capacity, high-voltage active power filters (APFs), crucial harmonic mitigation devices in power systems, have a direct impact on the power quality and operational efficiency of the grid. However, traditional grid status assessment methods often focus on monitoring and analyzing a single electrical parameter, failing to fully reflect the complex dynamic behavior and economics of the APF and the entire grid.

[0003] Deficiencies in the existing technology: Traditional methods can only process limited electrical parameter data and cannot fully reflect the complex dynamic behavior of the power grid. For active filter performance evaluation, there is a lack of analytical methods that comprehensively consider multi-dimensional data such as harmonic suppression rate, equipment energy consumption, and maintenance costs. Traditional methods have difficulty accurately assessing the stability of equipment under complex operating conditions, cannot predict equipment failure risks in advance, and lack effective test signals and evaluation indicators to quantify the risk of dynamic instability of equipment. Grid companies, users, and regulators often lack comprehensive information and scientific decision-making support when making decisions. Traditional methods fail to consider the strategic interactions of multiple stakeholders, resulting in suboptimal decision-making outcomes. Traditional regulatory reports are easy to forge and tamper with, lack effective anti-counterfeiting measures, and the data and decision-making processes in regulatory reports lack traceability, making effective supervision and auditing difficult.

[0004] In view of the above shortcomings, the present invention proposes a grid status assessment method and system based on a large-capacity high-voltage active filter structure. Summary of the Invention

[0005] The present invention provides a grid status assessment method and system based on a large-capacity, high-voltage active filtering structure. Through interdisciplinary integration, it achieves breakthroughs in the fields of economic quantification of harmonic control, dynamic risk assessment, multi-party collaborative decision-making, and data security.

[0006] The first aspect of the present invention provides a grid state assessment method based on a large-capacity high-voltage active filter structure, the grid state assessment method based on the large-capacity high-voltage active filter structure includes: obtaining the harmonic suppression rate of each harmonic, 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 the time window; mapping the harmonic economic impact factor matrix into a quantum bit topology network, solving the network energy minimum state through a quantum annealing processor, identifying high-energy nodes and outputting a quantum risk heat map; injecting a chaotic test signal into the active filter, collecting the voltage response signal at the output end, and calculating the Lyapunov equation of the voltage response signal. The index spectrum is obtained, and the maximum positive index value is selected as the criterion for equipment stability, and the equipment health attenuation curve is output; based on the quantum risk heat map and the equipment health attenuation curve, a three-party game tree of power grid enterprises, users and regulatory agencies is constructed, and the Nash equilibrium point is solved by reverse induction, and the Nash equilibrium decision path is output; based on the Nash equilibrium decision path, it is converted into the Hamiltonian of the spin glass system, and the simulated annealing algorithm is used to solve the system ground state, and the anti-interference asset allocation table is output; the anti-interference asset allocation table is encoded into the photon orbital angular momentum state, and data topology protection is achieved through an optical ring cavity to generate a quantum anti-counterfeiting supervision report, which includes the harmonic governance efficiency-cost ratio, the dynamic electricity price strategy compliance index and the multi-party responsibility traceability chain.

[0007] Optionally, in a first implementation method of the first aspect of the present invention, it includes: collecting the harmonic suppression rate of each order of the active filter, the real-time electricity price fluctuation data of the power grid node, the equipment maintenance cost record and the life cycle historical data to obtain the original data stream with time stamp alignment; calculating the sensitivity coefficient of the real-time electricity price to the harmonic disturbance according to the power market trading rules of the area where the power grid node is located, and obtaining a dynamic electricity price sensitivity coefficient table; constructing an equipment life attenuation model based on the accumulated operating time and historical maintenance records of the active filter, and obtaining the equipment life attenuation weight vector; inputting the original data stream, the sensitivity coefficient table, and the attenuation weight vector into a matrix generator, verifying the matrix data integrity through an optical topology checker, generating a digital fingerprint with a timestamp, and obtaining a verifiable harmonic economic impact factor matrix.

[0008] Optionally, in a second implementation method of the first aspect of the present invention, it includes: mapping the harmonic economic impact factor matrix into quantum bit nodes, converting the matrix element values ​​into the coupling strength between quantum bits, defining the initial state of the quantum bit as the direction of the superconducting ring current, clockwise represents low risk, and counterclockwise represents high risk, to obtain a quantum coupling network topology map; inputting the quantum coupling network topology map into a quantum annealing processor, applying a transverse magnetic field and gradually reducing the magnetic field intensity, and converging the system to the lowest energy state through the quantum tunneling effect, recording the final spin direction of each node, and obtaining a quantum state distribution table after annealing; extracting the energy value in the quantum state distribution table after annealing 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, representing the risk level with different color depths, and obtaining an interactive quantum risk heat map.

[0009] Optionally, in a third implementation method of the first aspect of the present invention, it 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, and obtaining a chaotic response data set; reconstructing the output signal in the chaotic response data set in phase space, and calculating its maximum Lyapunov exponent λmax: when λmax>0, it is determined that the equipment is at risk of dynamic instability; the absolute value of the exponent is inversely proportional to the health of the equipment; obtaining an equipment stability criterion table; establishing a baseline health curve based on the historical maintenance records of the equipment, and adopting a two-parameter exponential decay model: health score H(t)=H0×e^(-αt)+β×maintenance times; wherein α is dynamically corrected by λmax, and β is determined by the quantitative value of the maintenance effect; obtaining a dynamic attenuation model parameter set; inputting the dynamic attenuation model parameters into the failure time predictor, solving the time point when the health score first falls below the safety threshold, and obtaining the equipment health decay curve.

[0010] Optionally, in a fourth implementation method of the first aspect of the present invention, it includes: extracting high-risk time periods and frequency bands based on the quantum risk heat map, generating equipment replacement budget allocation constraints for power grid enterprises, setting the time window for user electricity contract adjustment based on the key failure time nodes in the equipment health attenuation curve, retrieving the dynamic compliance threshold initial value from the power market regulatory rule library as the regulatory agency's policy baseline; obtaining a three-party strategy space table; converting the risk level in the quantum risk heat map into the economic loss weight of the power grid enterprise, mapping the equipment health score to the cost sensitivity coefficient of the user electricity contract adjustment, defining the positive correlation between the regulatory agency's compliance threshold adjustment step and the risk level; obtaining the game tree node parameter mapping Shooting table; backtracking from the terminal node of the game tree, calculate the utility function value of each strategy combination layer by layer: grid enterprise utility: equipment maintenance cost reduction + power supply quality improvement benefit; user utility: electricity bill reduction - electricity contract adjustment cost; regulatory agency utility: compliance rate improvement - audit cost; obtain a set of candidate equilibrium strategies; screen solutions that meet the following conditions from the candidate equilibrium strategy set: grid enterprise 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; obtain the Pareto optimal solution set; according to the time window distribution of the quantum risk heat map, sort the strategies in the Pareto optimal solution set by execution urgency, and obtain the Nash equilibrium decision path.

[0011] Optionally, in a fifth implementation method of the first aspect of the present invention, it includes: mapping each strategy node in the Nash equilibrium decision path to a spin direction: spin up represents the execution of the asset allocation decision, and spin down represents the suspension of execution; defining the spin interaction strength according to the economic correlation between strategies, and obtaining a spin Hamiltonian parameter table; updating the spin direction through Monte Carlo sampling at each temperature point until the system energy converges, obtaining a ground state spin distribution diagram, and marking the final direction and energy value of all spins; sorting the nodes with spin up in the ground state spin distribution diagram according to the following rules: giving priority to 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 timing list; counting the execution ratios of relevant strategies of power grid enterprises, users, and suppliers in the ground state spin distribution, allocating the total maintenance cost proportionally, and obtaining a dynamic cost sharing ratio table; matching the equipment replacement timing list with the terms in the power market regulatory rule base to obtain an anti-interference asset allocation table.

[0012] Optionally, in a 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 into a photon orbital angular momentum state through a spatial light modulator, and obtaining a topologically protected photon data stream; extracting the unit time power quality loss cost from the harmonic economic impact factor matrix, and calculating the CBR value in combination with the maintenance cost sharing ratio: CBR=total value of power quality improvement / total maintenance cost; obtaining a dynamic CBR curve; obtaining real-time electricity price data from the power market trading platform, and comparing it with the theoretical electricity price in the Nash equilibrium decision path. Ratio: compliance index = 1-|actual electricity price-equilibrium electricity price| / equilibrium electricity price; obtain the electricity price compliance index table; encode the three-party game strategy execution record into a photon entangled state pair, assign a unique OAM entangled state identifier to each subject, and quantify the strategy contribution weight by the correlation strength of the entangled photon pair to obtain the optical topological responsibility traceability chain; input the topological protection photon data stream, dynamic CBR curve, electricity price compliance index table and optical topological responsibility traceability chain into the report synthesizer, realize irreversible data binding through the optical ring cavity, generate a machine-readable regulatory report, and obtain a quantum anti-counterfeiting regulatory report.

[0013] The second aspect of the present invention provides a power grid state assessment device based on a large-capacity high-voltage active filter structure, and the power grid state assessment device based on the large-capacity high-voltage active filter structure includes: an acquisition module for obtaining the harmonic suppression rate of each harmonic, 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 the time window; a processing module for mapping the harmonic economic impact factor matrix into a quantum bit topology network, solving the network energy minimum state through a quantum annealing processor, identifying the high-energy node output quantum risk heat map; an evaluation module for injecting a chaotic test signal into the active filter, collecting the voltage response signal at the output end, and calculating the Lyapunov equation of the voltage response signal. The quantum risk heat map and the equipment health attenuation curve are used to select the maximum positive exponent value, and the equipment health attenuation curve is output; the decision module is used to construct a three-party game tree of power grid enterprises, users and regulatory agencies based on the quantum risk heat map and the equipment health attenuation curve, solve the Nash equilibrium point by reverse 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, use the simulated annealing algorithm to solve the system ground state, and output the anti-interference asset allocation table; the reporting module is used to encode the anti-interference asset allocation table into the photon orbital angular momentum state, realize data topology protection through the optical ring cavity, and generate a quantum anti-counterfeiting supervision report, which includes the harmonic governance efficiency-cost ratio, the dynamic electricity price strategy compliance index and the multi-party responsibility traceability chain.

[0014] A third aspect of the present invention provides a power grid state assessment device based on a large-capacity high-voltage active filter structure, comprising: a memory and at least one processor, wherein the memory stores instructions; the at least one processor calls the instructions in the memory so that the power grid state assessment device based on the large-capacity high-voltage active filter structure executes the above-mentioned power grid state assessment method based on the large-capacity high-voltage active filter structure.

[0015] A fourth aspect of the present invention provides a computer-readable storage medium having instructions stored therein, which, when executed on a computer, enables the computer to execute the above-mentioned grid state assessment method based on a large-capacity high-voltage active filter structure.

[0016] The technical solution provided by the present invention has the following beneficial effects: By using the harmonic economic impact factor matrix, the harmonic frequency band suppression rate is directly mapped to the power quality loss cost. Combined with the quantum annealing algorithm, high-risk periods (peak economic losses corresponding to high-order harmonic frequency bands) are identified, allowing power grid companies to allocate maintenance budgets in a targeted manner. When the economic loss weight of high-frequency harmonics is high, the corresponding filter module is upgraded first. The device's potential instability is stimulated by chaotic test signals (based on the Lorentz equations) and quantified by the Lyapunov exponent spectrum. When the maximum positive exponent λmax > 0, the α parameter in the device health decay curve increases dynamically, triggering maintenance strategies in advance to avoid sudden failures. The three-party game tree is mapped to the Hamiltonian of a spin glass system, and the ground state distribution is solved through Monte Carlo simulated annealing. When there is resource competition between the equipment replacement costs of power grid companies and the energy efficiency reward and penalty thresholds of users, the negative interaction strength drives strategic coordination (users accept short-term electricity price fluctuations in exchange for long-term power supply quality improvements). Using photon orbital angular momentum (OAM) encoding, an optical topological accountability chain is generated, with each entity (grid company, user) assigned a unique entangled state identifier. If a user violates their electricity contract, the correlation strength of their entangled photon pairs will drop abnormally, allowing the responsible party to be quickly identified. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 Schematic diagram of an embodiment of a power grid status assessment method based on a large-capacity high-voltage active filter structure in an embodiment of the present invention; Figure 2 Schematic diagram of another embodiment of a power grid status assessment method based on a large-capacity high-voltage active filter structure according to an embodiment of the present invention; Figure 3 Schematic diagram of an embodiment of a power grid status assessment device based on a large-capacity high-voltage active filter structure in an embodiment of the present invention; Figure 4Schematic diagram of an embodiment of a power grid status assessment device based on a large-capacity high-voltage active filter structure in an embodiment of the present invention. DETAILED DESCRIPTION

[0018] The embodiments of the present invention provide a grid status assessment method and system based on a large-capacity, high-voltage active filtering structure. Through interdisciplinary integration, breakthroughs have been achieved in the fields of economic quantification of harmonic control, dynamic risk assessment, multi-party collaborative decision-making, and data security.

[0019] For ease of understanding, the specific process of the embodiment of the present invention is described below. Figure 1 In one embodiment of the present invention, a method for evaluating a power grid state based on a large-capacity high-voltage active filter structure includes: 101. Real-time collection of harmonic suppression rates, equipment energy consumption data, and maintenance cost records for large-capacity, high-voltage active filters. Calculate the power quality loss cost per unit time for each harmonic frequency band (1-50th order). The formula is: loss cost = harmonic amplitude × grid node electricity price sensitivity coefficient × equipment life attenuation weight. Generate a harmonic economic impact factor matrix (HEIF matrix) based on the time window (1-minute granularity). The matrix rows represent harmonic frequency bands, the columns represent time windows, and the element values ​​are the economic loss weights of the corresponding frequency bands within the time window. It is understood that the execution subject of the present invention can be a power grid status assessment device based on a large-capacity high-voltage active filter structure, or a terminal or a server, and the specific implementation is not limited here. The embodiment of the present invention is described by taking the server as the execution subject as an example; It should be noted that data collection (10:00-10:05 UTC) includes: harmonic suppression rate: recording the 1st-50th harmonic suppression rate (5th harmonic suppression rate 92%) using fiber optic current sensors; equipment energy consumption: recording active power loss (phase A 5.2kW, phase B 5.0kW, phase C 5.3kW); maintenance cost: accessing the maintenance database to obtain the latest three maintenance records (filter module replacement cost $12,000 per time); Loss cost calculation (taking the 10:01 minute window as an example): Harmonic frequency band Amplitude (A) Node electricity price sensitivity coefficient Lifespan decay weight Loss cost ($) 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 HEIF matrix construction (time window: 2023-10-01T10:00 to 10:05), matrix dimensions: 50 rows (1st-50th harmonics) × 5 columns (time stamp per minute), matrix element values ​​are rounded to four decimal places, economic loss weight value = normalized (loss cost × 10^3), rounded to three significant figures; Among them, time synchronization: IRIG-B time code is used to achieve μs-level synchronous acquisition; sensitivity coefficient: dynamically calculated based on the IEEE1547 standard, ranging from [0.1, 0.5]; life weight: the device aging rate is calculated according to the Arrhenius model, and the temperature sampling interval is 30 seconds; 102. Map the harmonic economic impact factor matrix into a quantum bit topology network, where each quantum bit corresponds to a matrix element; solve the network's lowest energy state using a quantum annealing processor and identify high-energy nodes (i.e., time periods and frequency bands with high risk of economic losses); output a quantum risk heat map, annotated in three-dimensional coordinate form: X-axis: harmonic frequency band (1-50th order), Y-axis: time window (UTC timestamp), Z-axis: risk level (1-10, calculated by normalization of quantum annealing energy values); It should be noted that, taking the HEIF matrix of a high-voltage active filter at 2025-03-21T10:00-10:05 as an example (50×5 matrix), some data are selected: 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 Each matrix element is mapped to a topological qubit, where the loss weight value is converted to the qubit's spin direction (loss weight ≥ 0.2 is mapped to spin-up, otherwise it is mapped to spin-down). Through the topological structure of Microsoft's Majorana quantum chip, interaction edges are established between qubits in adjacent time windows and harmonic frequency bands, with the weight determined by the economic correlation between the elements of the HEIF matrix (the correlation coefficient for adjacent time windows is 0.8, and the correlation coefficient for adjacent frequency bands is 0.5).

[0020] Quantum annealing was used to solve the lowest energy state, using a digital annealing algorithm with the following annealing parameters: initial temperature 1000K, cooling rate 0.95, and iterations 1000. By solving 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. Risk heat map output, 3D coordinate data example: X-axis (harmonic frequency) Y-axis (timestamp) Z axis (risk level) 5 2025-03-21T10:02:00Z 8 23 2025-03-21T10:03:00Z 6 Normalization method: The quantum annealing energy value is proportionally scaled to levels 1-10 according to the maximum value (0.25), with an energy difference of 0.025 corresponding to a risk level of 1. The heat map is rendered using WebGL, with a color gradient from green (low risk) to red (high risk). Clicking allows users to query the economic loss weight and associated device parameters of a specific node. Among them, topological mapping: Using the King'sGraph network structure, each quantum bit is coupled with four adjacent nodes to simulate the propagation path of power grid harmonics. Annealing optimization: Based on the simulated annealing algorithm of Hunan University's low-power annealing chip, a single solution takes less than 50ms. Dynamic update: The HEIF matrix is ​​synchronously updated every 5 minutes and the annealing calculation is triggered to ensure real-time performance.

[0021] 103. Inject a chaotic test signal (Lorentz attractor waveform) into the active filter and collect the voltage response signal at the output. Calculate the Lyapunov exponent spectrum of the response signal and select the maximum positive exponent value as the device stability criterion. Output the device health decay curve, which includes: the current health score (based on the convergence of the Lyapunov exponent), the performance prediction curve for the next 30 days (fitted by the exponential decay model), and the critical failure time node (the estimated time when the health falls below the safety threshold). It should be noted that in a steel plant's 10kV cascaded APF (three-phase Y-connection, 10 IGBT units per phase), a Lorenz attractor waveform test signal was injected with the following parameters: chaos parameters: σ=10, ρ=28, β=8 / 3; injection voltage amplitude: 5% of the rated voltage (575V), duration: 30 seconds; Data acquisition: Use NIPXIe-5160 high-speed oscilloscope (sampling rate 2.5GS / s) to record the three-phase voltage waveform at the output of APF and generate time series data ( Figure 1 The voltage response waveform of phase A shows typical chaotic divergence characteristics).

[0022] Lyapunov exponent calculation, phase space reconstruction of phase A voltage response signal (embedding dimension m = 3, time delay τ = 5ms), and exponential spectrum calculation using Wolf algorithm: Index direction Index value (bit / s) λ1 +0.82 λ2 -0.15 λ3 -2.37 The maximum positive exponent λ1 = 0.82 indicates that the system is sensitive to the initial conditions and the device is in a metastable state.

[0023] Health decay curve generation: Current score: Based on λ1 convergence, score = 100 × (1-λ1 / λmax), λmax = 1.5 (threshold according to IEC61000-4-30), the score is 45.3 points (lower than the safety threshold of 60 points); 30-day prediction: The decay curve is fitted using an exponential model: H(t) = H0 × e^(-kt), k = 0.023 / day (R² = 0.98), and the score is predicted to drop to 30 points on the 25th day. Critical failure node: When the score is less than 30, an alert is triggered, and the expected failure time is the 28th day (confidence interval ±1.5 days). The test was conducted during the off-peak period of the power grid (23:00-02:00 UTC) to avoid production interference. The cooling system adopted a cascade unit independent air duct design ( Figure 3 structure), ensuring that the temperature rise during the test is ≤3°C; under abnormal operating conditions, SPLL technology is used to correct the grid phase offset in real time to ensure data accuracy.

[0024] 104. Based on the quantum risk heat map and the equipment health decay curve, a three-party game tree is constructed among the power grid company, the user, and the regulatory agency. The three-party strategy space is defined as follows: the power grid company: equipment replacement budget allocation plan and maintenance cycle strategy; the user: electricity contract adjustment range and energy efficiency reward and punishment acceptance threshold; the regulatory agency: dynamic compliance threshold setting rules and abnormal transaction audit intensity. The Nash equilibrium point is solved by reverse induction, and the Nash equilibrium decision path is output, including: the Pareto optimal solution set (the balance point combination of technical parameters and economic indicators) and the priority list of each subject's strategy execution. It should be noted that the quantum risk heat map selects a high-risk node (5th harmonic risk level 8, corresponding to a maintenance cost of $12,000 / time) from 10:02-10:03 on March 21, 2025. The health curve shows that the device currently scores 45.3 points, and a failure warning is predicted to be triggered on the 25th day (the maintenance cycle strategy must be completed within 25 days). Game tree construction, definition of the three-party strategy space and payoff function (unit: thousands of US dollars): 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 and punishment threshold × 8 Regulatory agencies Compliance threshold γ (lower limit of risk level) γ∈[4,7] Benefit = Audit Intensity × 3 - Deviation Penalty × 5 Using backward induction, we solve the problem at the third level of the game tree (regulatory decision): when γ = 6, the audit intensity = 0.8, the deviation penalty = 0.4, and the maximum benefit is 12.4. Backtracking to the second level (user decision): when β = +0.15, the user benefit is 6.3 and the γ = 6 constraint is satisfied. The final equilibrium point: the power grid company chooses α = 0.55 (55% of the budget for new equipment), shortening the maintenance cycle to 20 days. The user accepts the contract adjustment of β = +0.15 (a 15% price increase). The regulator sets γ = 6 (risk levels ≥ 6 require mandatory intervention). Output results, Pareto optimal solution set: Technical parameters: harmonic suppression rate ≥ 95%, maintenance cycle ≤ 20 days; Economic indicators: total cost ≤ $15k, user electricity fee increase ≤ 18%; Policy priority: Power grid companies prioritize replacing 5th harmonic filter modules (devices associated with the 10:02 high-risk node); users implement dynamic electricity price increases during the 10:00-11:00 period; regulatory agencies initiate real-time audits of nodes with a risk level ≥ 6; The risk cost weight = risk level × 1.5k / level (8 risk level corresponds to 12k cost); the energy efficiency reward and penalty thresholds are set according to the IEEE 2030.5 protocol, with an acceptance threshold of ±15%; the backward induction method uses a three-stage dynamic game model, with a computation time of <200ms (based on the Gurobi optimizer); 105. Based on the Nash equilibrium decision path, the system is converted into a spin glass system Hamiltonian, where each spin represents an asset allocation decision (equipment replacement, budget allocation), and the strength of the spin interaction is determined by the economic correlation between the decisions. A simulated annealing algorithm is used to solve the system ground state and output an anti-interference asset allocation table, including: equipment replacement time sequence (accurate to weekly granularity), maintenance cost sharing ratio (shares among power grid companies, users, and suppliers), and regulatory compliance verification labels (compliant / pending correction / violation). 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 the 12th week, 1: executed in the 8th week); Spin 2 (S2): budget allocation ratio (0: grid company 60%, 1: user-led allocation); Spin 3 (S3): maintenance cycle (0: 20 days, 1: 15 days); Construct the Hamiltonian: ; Among them: Economic correlation coefficient: J 12 =0.8 (equipment replacement is strongly correlated with budget), J 13 =0.5, J 23 =0.3; local field coefficients: h1=1.2 (device delay cost), h2=0.9 (user budget sensitivity), h3=0.6 (compliance pressure); Simulated annealing was used for the solution. Parameters were set as follows: initial temperature T0 = 1000, cooling rate α = 0.95, and number of iterations 1000. Candidate solution generation: neighborhood solutions were generated by bit flipping (011 → 111). Annealing process key data: 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 Output anti-interference configuration table, the base state solution is S=(1,0,1), corresponding to: Configuration items Parameter value Economic connection verification Equipment replacement sequence Start in week 8 (IGBT modules first) Comply with GB / T14549 Cost sharing ratio Grid 60%, users 25%, suppliers 15% To be revised (user threshold 30%) Compliance Label conform to Audit intensity 0.7 Among them, the interaction strength calculation is based on the marginal benefit difference of the Pareto solution in the game tree, quantified by the Pearson correlation coefficient; annealing optimization: an adaptive neighborhood search strategy is introduced. When there is no improvement after 50 consecutive iterations, the temperature is temporarily increased to 1.2Tcurrent to escape the local optimum; compliance verification: a correction warning is triggered for items with excessive apportionment ratios; 106. Encode the anti-interference asset allocation table as a photon orbital angular momentum state, and implement data topology protection through an optical ring cavity; generate a quantum anti-counterfeiting regulatory report, including: Harmonic Governance Cost-Ratio (CBR): Calculate the power quality improvement value per unit cost based on the harmonic economic impact factor matrix; Dynamic Electricity Price Strategy Compliance Index: Compare the deviation between the real-time electricity price and the Nash equilibrium solution; Multi-party Responsibility Traceability Chain: Record the decision-making contribution weight of each subject through the photon orbital angular momentum entangled state; It should be noted that, taking the anti-interference asset allocation table of a certain power grid in Q3 2025 as an example (equipment replacement sequence: Week 8; cost sharing ratio: power grid 60%, user 25%, supplier 15%): The configuration table is converted into photon orbital angular momentum states, with each configuration item corresponding to an OAM mode (topological charge number l = ±8, ±25, ±15). Through the topological defect lattice structure developed by the Nankai University team, dual topological protection is achieved: momentum space protection: using chiral symmetry to generate non-trivial entangled states to ensure that the OAM mode maintains phase consistency during transmission (phase error <0.1π); real space protection: using a ring photonic lattice (C8 symmetry) to constrain high-order vortex modes and suppress mode crosstalk (crosstalk rate <1e-4); Data topology protection uses an optical ring cavity (5cm diameter, Q value >1e6) to store encoded data, leveraging vortex coordinated rotational symmetry to achieve: dynamic stability: OAM mode power fluctuation <0.5dB when temperature fluctuates ±2°C; anti-interference performance: bit error rate <1e-9 against electromagnetic interference (30dBm) and mechanical vibration (50Hz); The quantum anti-counterfeiting report output generates core indicators based on the HEIF matrix and Nash equilibrium solution from September 1 to September 7, 2025: index Calculation method and numerical examples Harmonic Control Cost-to-Ratio (CBR) Power quality improvement (ΔTHD = 3.2%) / maintenance cost (15k) → CBR = 213 - 1 Dynamic Electricity Price Compliance Index The normalized deviation of the real-time electricity price (12.7%) → index 62.3 (threshold > 60 for compliance) Multi-party responsibility traceability chain Contribution weights of orbital angular momentum entangled state records: power grid (58.7%), users (24.3%), suppliers (17.0%) The CBR calculation is based on the economic loss weight of the 5th harmonic in the HEIF matrix (0.215 / minute) and the improvement in suppression rate after equipment replacement (92% → 95%) to quantify the unit cost improvement. Photon entangled state: Bell states are constructed using OAM modes with l=8 and l=15, and the quantum immutability of the tripartite weights is verified through coincidence counting measurements (CHSH inequality S=2.72>2). Topology verification tag: A vortex filter is inserted at the output of the ring cavity to extract the l=±25 mode to verify the compliance of the user sharing ratio (deviation from the preset value is <1.5%). In this embodiment, real-time data collection and processing accurately calculate harmonic economic losses, providing a basis for optimized decision-making for power grid companies. Quantum computing and risk identification technologies can quickly identify high-risk periods and frequency bands, facilitating timely mitigation measures and reducing economic losses. Chaos testing can assess equipment stability and predict equipment failure times, providing a scientific basis for maintenance planning. Equipment health decay curves can intuitively reflect equipment performance trends, facilitating timely maintenance measures. Three-party game trees and Nash equilibrium solution techniques can balance the interests of power grid companies, users, and regulators, achieving an optimal combination of technical parameters and economic indicators. Policy priority lists can guide all parties to execute decisions sequentially, improving overall efficiency. A simulated annealing algorithm can solve the ground state of a spin glass system and output an anti-interference asset allocation table to ensure the economic and regulatory 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 technology can achieve topological protection of data, improving its anti-interference and reliability. Quantum anti-counterfeiting regulatory reports can ensure the authenticity and immutability of data, providing strong support for supervision. In summary, by comprehensively applying a variety of advanced technologies, a comprehensive and detailed assessment and optimization of the power grid status is carried out, which improves the economy, stability and security of the power grid and provides a scientific basis for the decision-making of power grid companies.

[0025] See also Figure 2 Another embodiment of the grid state assessment method based on a large-capacity high-voltage active filter structure in the embodiment of the present invention includes: 201. Real-time collection of harmonic suppression rates, equipment energy consumption data, and maintenance cost records for large-capacity high-voltage active filters; calculation of the power quality loss cost per unit time for each harmonic frequency band (1-50th order) using the following formula: loss cost = harmonic amplitude × grid node electricity price sensitivity coefficient × equipment life attenuation weight; generation of a harmonic economic impact factor matrix (HEIF matrix) based on a time window (1-minute granularity), with the rows representing harmonic frequency bands, the columns representing time windows, and the element values ​​representing the economic loss weights of the corresponding frequency bands within the time window; Specifically, multi-source heterogeneous data is integrated in real time: hardware sensors are used to collect the suppression rates of each harmonic of the active filter in real time, and the real-time electricity price fluctuation data of the grid nodes is obtained simultaneously. The equipment maintenance cost records and life cycle historical data are extracted from the enterprise resource planning system to obtain the original data stream aligned with the timestamp (a triple sequence containing harmonic amplitude, electricity price fluctuation value, and maintenance cost value); dynamic electricity price sensitivity coefficient calibration: according to the electricity market trading rules of the area where the grid node is located, the sensitivity coefficient of the real-time electricity price to harmonic disturbance is calculated: when the harmonic distortion rate exceeds the threshold, the electricity price penalty mechanism is triggered, and the sensitivity coefficient increases according to the penalty gradient; the sensitivity coefficient value range is set to [0.1, 5.0], and the gradient table is determined by the experimental calibration method; output product: dynamic electricity price sensitivity coefficient table (including the mapping relationship between timestamp, harmonic frequency band, and sensitivity coefficient); dynamic update of equipment life attenuation weight: based on the accumulated operating time of the active filter and historical maintenance records, construct an equipment life attenuation model: define the basic attenuation curve as an exponential function, and the attenuation rate is corrected by the maintenance interval length; reset the attenuation rate parameter after each maintenance operation; output product: equipment life attenuation weight vector (a sequence of weight values ​​updated at a granularity of 1 minute); HEIF matrix generation and verification: input the original data stream, sensitivity coefficient table, and attenuation weight vector into the matrix generator; fill in the matrix elements according to the following rules: row index: harmonic frequency band (1-50 times); column index: time window (UTC time, 1 minute granularity) element value: the power quality loss cost caused by the corresponding harmonic frequency band in the current time window, calculated as: loss cost = harmonic amplitude × current sensitivity coefficient × current attenuation weight; verify the integrity of the matrix data through an optical topology checker, and generate a digital fingerprint with a timestamp; the final product: a verifiable HEIF matrix, which serves as the only input source for generating the quantum risk heat map.

[0026] It should be noted that the following is an example of real-time data collection and HEIF matrix generation for a large-capacity high-voltage active filter from 10:00 to 10:05 (UTC) on March 20, 2025: Real-time fusion of multi-source heterogeneous data and harmonic data collection: Embedded current sensors (accuracy ±0.5%) monitor the 1st-50th harmonic amplitudes in real time. At 10:00:00: the 5th harmonic amplitude = 0.3A, the 7th = 0.25A, and the 11th = 0.18A (other frequency bands are below the threshold and ignored); at 10:01:00: the 5th = 0.35A, and the 7th = 0.28A (grid load abrupt change). Electricity price data synchronization: Access the electricity market API to obtain real-time electricity price fluctuation data: the electricity price sensitivity coefficient from 10:00 to 10:05 is 1.8 (because the current period is peak electricity consumption, the penalty gradient increases).

[0027] Maintenance cost extraction: Retrieve equipment life cycle data from the ERP system: cumulative operating time = 12,000 hours, last maintenance time = March 1, 2025, maintenance cost = 5,000 yuan / time.

[0028] Dynamic electricity price sensitivity coefficient calibration, penalty gradient rule: Harmonic distortion rate (%) Sensitivity coefficient <5 0.1 5-10 1.8 >10 5.0 The 5th harmonic distortion rate at 10:01:00 is 6.2%, and the triggered electricity price sensitivity coefficient is 1.8.

[0029] The equipment life decay weight is dynamically updated. Decay model parameters: Basic decay formula: Weight = e^(-0.0001*t), reset to 0 after maintenance. Currently, t = 480 hours (since the last maintenance), weight = 0.95.

[0030] Dynamic correction: Due to the sudden load change at 10:01:00, the weight decay is accelerated by 10%. The corrected weight = 0.95×0.9=0.855.

[0031] HEIF matrix generation and verification, matrix filling example (partial data from 10:00:00-10:01:00): Harmonic frequency band Time Window Harmonic amplitude Sensitivity coefficient Decay Weight Loss cost (yuan) 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 Data integrity verification: An optical topology checker compares the timestamp-aligned triplet sequence (harmonics, electricity price, weight) to generate a digital fingerprint SHA-256: a1b2c3... If any data is missing or abnormal, an alarm is triggered and resampling is performed.

[0032] Output product, HEIF matrix structure: row index: 1-50 harmonics (only valid frequency bands are displayed); column index: 2025-03-20T10:00 to T10:05 (one column per minute); Element value example: [5,T10:00]=0.513 yuan; [5,T10:01]=0.538 yuan; The matrix quantifies the economic impact of different harmonic frequency bands, among which the fifth harmonic at 10:01 causes the highest loss cost (0.538 yuan) and needs to be treated first.

[0033] 202. Map the harmonic economic impact factor matrix into a quantum bit topology network, where each quantum bit corresponds to a matrix element; solve the network's lowest energy state using a quantum annealing processor and identify high-energy nodes (i.e., time periods and frequency bands with high economic loss risk); output a quantum risk heat map, annotated in three-dimensional coordinate form: X-axis: harmonic frequency band (1-50th order), Y-axis: time window (UTC timestamp), Z-axis: risk level (1-10, calculated by normalization of quantum annealing energy values); Specifically, the quantum bit topological network is constructed by mapping the HEIF matrix elements to quantum bit nodes, converting the matrix element values ​​into the coupling strength between quantum bits, defining the initial state of the quantum bit as the direction of the superconducting ring current, clockwise represents low risk, and counterclockwise represents high risk, and obtaining the quantum coupling network topology diagram (including node connection weights and initial state distribution); quantum annealing energy optimization: inputting the quantum coupling network topology diagram into the quantum annealing processor, applying a transverse magnetic field and gradually reducing the magnetic field intensity, and converging the system to the lowest energy state through the quantum tunneling effect, recording the final spin direction of each node, and obtaining the quantum state distribution table after annealing (including node number, Spin direction, energy value); Risk level normalization mapping: Extract the energy value from the annealed quantum state distribution table and convert it 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 percentiles, 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 sequence of photon polarization states; Generate a holographic projection through a spatial light modulator, and represent the risk level with different color depths; Final product: An interactive quantum risk heat map (supporting touch zoom and risk traceability query) serves as the only input source for the multi-agent game decision-making model in step 4.

[0034] It should be noted that the following is an example of converting the HEIF matrix (partial data) to a quantum risk heat map for a large-capacity high-voltage active filter from 10:00 to 10:05 (UTC) on March 20, 2025: Quantum bit topology network construction, HEIF matrix input: HEIF matrix dimensions: 50 rows (harmonic frequency band 1-50) × 5 columns (time window 10:00-10:05), element value is the economic loss weight (unit: yuan / minute). Sample data: Harmonic frequency 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 Quantum bit mapping rule: Each matrix element is mapped to a quantum bit, for a total of 250 quantum bits. The matrix element value (economic loss weight) is converted to the coupling strength between quantum bits using the formula: ;

[0035] For example, the HEIF value of the 5th harmonic at 10:03 is 0.892 yuan, and the coupling strength with the adjacent time window (10:02) is: ;

[0036] Quantum State Initialization: Define the initial circular current direction of the superconducting qubit: clockwise (low risk): HEIF value < 0.5 yuan; counterclockwise (high risk): HEIF value ≥ 0.5 yuan; the initial state of the 5th harmonic qubit at 10:03 is counterclockwise (high risk). Quantum annealing energy optimization: annealing parameters: initial transverse magnetic field strength: 100mT, cooling rate: 1mT / millisecond, annealing duration: 5ms. Quantum tunneling effect: At the 10:03 time window, the high coupling strength (J=0.0007565) at the 5th harmonic node triggers quantum tunneling, flipping the spin direction from counterclockwise to clockwise (energy reduction).

[0037] Annealing results: Quantum Node (Harmonic-Time) Final spin direction Energy value (eV) 5 times - 10:03 Counterclockwise 8.92 7 times - 10:03 Counterclockwise 7.01 11th - 10:03 Clockwise 2.05 Normalized risk level mapping, energy value sorting: highest energy node: 5 times - 10:03 (8.92eV, top 5%), marked as level 10 risk; lowest energy node: 11 times - 10:00 (2.05eV, bottom 5%), marked as level 1 risk. Level division: Energy value is divided into percentile segments: Percentile range Risk Level Color Coding 95-100% Level 10 red 85-95% Level 9 orange color ... ... ... 0-5% Level 1 blue Optical topology visualization and rendering, photon polarization encoding: Mapping risk level (Z-axis) to photon polarization angle: θ = risk level / 10 × 180°; a risk level of 10 corresponds to a polarization angle of 180°, with the photon polarization state in the horizontal direction (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 highlighted area at 10:03, with touch and zoom support for detailed parameters (economic loss weight, maintenance cost correlation). Output: Quantum Risk Heat Map: X-axis: 1-50th harmonic (5th and 7th highlighted); Y-axis: 2025-03-20 T10:00 to T10:05; Z-axis: Risk level (5th - 10:03 is marked as Level 10). The heat map shows that the fifth harmonic presents the highest risk at 10:03 (red area), requiring priority adjustment of filtering strategies or arrangement of equipment maintenance.

[0038] 203. Inject a chaotic test signal (Lorentz attractor waveform) into the active filter and collect the voltage response signal at the output. Calculate the Lyapunov exponent spectrum of the response signal and select the maximum positive exponent value as the device stability criterion. Output the device health decay curve, which includes: the current health score (based on the convergence of the Lyapunov exponent), the performance prediction curve for the next 30 days (fitted by the exponential decay model), and the critical failure time node (the estimated time when the health falls below the safety threshold). Specifically, chaotic test signal generation and injection: Based on the Lorentz equations, a test signal with chaotic characteristics is generated. Its waveform meets the following requirements: it is non-periodic and its spectrum covers the 1st-50th harmonic range; its peak voltage is 10%-15% of the rated input voltage of the active filter; the test signal is injected into the main circuit of the active filter through an isolation transformer, with a duration of ≤100ms; the output product is: a chaotic response data set (including the injected signal waveform and the output voltage response time series); Lyapunov exponent spectrum calculation: the output signal in the chaotic response data set is reconstructed in phase space and its maximum Lyapunov exponent λmax is calculated: when λmax>0, it is determined that the device is at risk of dynamic instability; the absolute value of the exponent is inversely proportional to the health of the device; the output product is: a device stability criterion table (including the λmax value and convergence analysis results); Construction of health decay model: A baseline health curve is established based on the historical maintenance records of the equipment, and a two-parameter exponential decay model is adopted: health score H(t)=H0×e^(-αt)+β×maintenance times; where α is dynamically corrected by λmax, and β is determined by the quantitative value of the 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 the time point when the health score first falls below the safety threshold: the safety threshold is retrieved from the IEEEStd18-2020 standard library according to the equipment model; the prediction time granularity is accurate to the hour level; the final product: equipment health decay curve (including current score, prediction curve for the next 30 days and key failure nodes), which serves as the direct input of the multi-agent game decision model in step 4.

[0039] It should be noted that the following is an example of chaos testing and health assessment of a large-capacity high-voltage active power filter (model APF-10kV / 500A) at 10:00 on March 20, 2025: Chaotic test signal generation and injection, Lorentz attractor waveform generation: Chaotic signals are generated based on the Lorentz equations (parameters σ = 10, ρ = 28, β = 8 / 3). The spectrum covers harmonics 1-50, meeting aperiodicity requirements. Peak voltage = 12% × rated voltage (10 kV) = 1.2 kV, injected into the main circuit through an isolation transformer for 80 ms.

[0040] Output response acquisition: The voltage signals at the injection point and the output end are collected to generate a chaotic response data set (time series length = 10,000 points, sampling rate = 1 MHz).

[0041] Lyapunov exponent spectrum calculation and phase space reconstruction: The phase space was reconstructed using a delayed embedding method (delay time τ = 5ms, embedding dimension m = 3) to generate a three-dimensional trajectory matrix. Maximum Lyapunov exponent (λmax) calculation: The output signal divergence was calculated using the Wolf algorithm, yielding λmax = 0.52 (a value > 0 indicates a risk of dynamic instability).

[0042] Health decay model construction and baseline health curve: Based on historical maintenance records (average maintenance intervals of 180 days over the past three years), the initial health H0 = 95 (out of a maximum score of 100). A two-parameter exponential decay model: H(t) = 95 × e^(-0.0023t) + 0.15 × number of maintenance attempts (β is determined by the quantified maintenance effect; this maintenance effect score is 0.15). α dynamic correction: Because λmax = 0.52 > the threshold of 0.3, α is triggered = base value × 1.2 = 0.00276. Critical failure time prediction and safety threshold setting: According to IEEE Std 18-2020, the equipment health safety threshold is 60. Failure time calculation: Solving the equation 60 = 95 × e^(-0.00276t) + 0.15 × 3 (cumulative number of maintenance attempts) yields t ≈ 112 days, meaning the expected failure time is July 10, 2025, ± 6 hours.

[0043] Output product, device health decay curve: Current health: 82 points (2025-03-20T10:00); 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 (healthiness falls below 60 points for the first time); The curve shows that the equipment health will drop below the safety threshold within the next three months, and it is recommended to schedule preventive maintenance before June 2025.

[0044] 204. Based on the quantum risk heat map and the equipment health decay curve, a three-party game tree is constructed among the power grid company, the user, and the regulatory agency. The three-party strategy space is defined as follows: the power grid company: equipment replacement budget allocation plan and maintenance cycle strategy; the user: electricity contract adjustment range and energy efficiency reward and punishment acceptance threshold; the regulatory agency: dynamic compliance threshold setting rules and abnormal transaction audit intensity. The Nash equilibrium point is solved by reverse induction, and the Nash equilibrium decision path is output, including: a Pareto optimal solution set (a balance point combination of technical parameters and economic indicators) and a priority list of each subject's strategy execution. Specifically, the three-party strategy space is dynamically constructed: based on the quantum risk heat map in step 2, high-risk time periods and frequency bands are extracted to generate the equipment replacement budget allocation constraints of the power grid company; according to the key failure time nodes in the equipment health attenuation curve in step 3, the time window for adjusting the user's electricity contract is set; the initial value of the dynamic compliance threshold is retrieved from the power market regulatory rule library as the strategy baseline of the regulatory agency; the output product is a three-party strategy space table (including the feasible strategy set and constraints of the power grid company, the user, and the regulatory agency); the physical parameter mapping of the game tree is used to convert the risk level (Z-axis value) in the quantum risk heat map into the economic loss weight of the power grid company; the equipment health score is mapped to the cost sensitivity coefficient of the user's electricity contract adjustment; the positive correlation between the compliance threshold adjustment step of the regulatory agency and the risk level is defined; the output product is a game tree node parameter mapping table (including the conversion rules from technical parameters to economic indicators); the reverse inductive equilibrium solution is used to trace back from the end node of the game tree and calculate the utility function value of each strategy combination layer by layer: the power grid company Utility: reduced equipment maintenance costs + improved power supply quality benefits; user utility: reduced electricity bills - electricity contract adjustment costs; regulatory agency utility: improved compliance rate - audit costs; output product: candidate equilibrium strategy set (including utility values ​​of each node and strategy combinations); Pareto optimal solution screening: screen solutions that meet the following conditions from the candidate equilibrium strategy set: power grid enterprise 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 technical-economic balance point and three-party utility values); dynamic sorting of strategy priorities: based on the time window distribution of the quantum risk heat map, the strategies in the Pareto optimal solution set are sorted by execution urgency: strategies corresponding to high-risk periods are marked as immediately executed; strategies corresponding to medium-risk periods are marked as executed this week; strategies corresponding to low-risk periods are marked as executed this month; final product: Nash equilibrium decision path (including strategy execution list and priority label), which serves as the only input source for spin glass theory optimization.

[0045] It should be noted that the following is an example of a power grid company's game decision-making based on the quantum risk heat map and equipment health decay curve on March 20, 2025: The three-party strategy space is dynamically constructed, with the following input data: Quantum risk heat map: 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 decay curve: Critical failure time = 2025-07-10T14:30. Strategy constraints: main body Strategy Space Constraints Power grid companies Equipment replacement budget allocation (Plan A: Replace the 5th harmonic filter module, costing 1.2 million yuan; Plan B: Delay replacement) Budget cap: 2 million user Adjustment range of electricity consumption contract (±10% load, acceptance threshold: adjustment cost ≤ 50,000 yuan / month) Time window: 2025-06-01 to 2025-07-10 Regulatory agencies Dynamic compliance threshold (harmonic distortion threshold adjusted from 5% to 4%, audit intensity increased by 20%) Adjustment step size is positively correlated with risk level Game tree physical parameter mapping, technical parameters → economic indicators: Technical Parameters Economic indicator mapping rules Example Value Risk level (Z-axis value) Economic loss weight of power grid enterprises = 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% (10-level risk → adjustment 2%) Threshold changed from 5% to 4% (adjusted by 1%) Reverse inductive equilibrium solution, strategy combination and utility calculation (some examples): Strategy Portfolio Utility of power grid enterprises (10,000 yuan) User utility (10,000 yuan) Regulatory agency effectiveness (% improvement in compliance rate) Plan A + User Adjustment + Regulatory Threshold 4% -120+100=-20 8-4.5=3.5 15% - 5% (audit costs) = 10% Plan B + No user adjustment + 5% regulatory threshold 0+0=0 0-0=0 5%-2%=3% Candidate equilibrium strategy set: 1. {Scheme A, user adjustment ±10%, regulatory threshold 4%} → utility (-20, 3.5, 10); 2. {Scheme B, user adjustment ±5%, regulatory threshold 4.5%} → utility (0, 2.1, 8); Pareto optimal solution screening and constraint verification: Solution number Power grid budget ≤ 2 million User adjustment cost ≤ 50,000 Regulatory step length matching risk level 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 0.5% to match level 7 risk (√) Pareto optimal solution set: Solution 1: Technical-economic equilibrium point (replacement of the 5th harmonic module, user adjustment ±10%, regulatory threshold 4%); Solution 2: delayed replacement, user adjustment ±5%, regulatory threshold 4.5%; Dynamically sort policy priorities and divide execution urgency: Strategy Associated risk period Execution priority Replace the 5th harmonic module High Risk (10:03) Execute immediately (red) User adjustable ±10% load 30 days before key failure Execution this week (orange) Regulatory threshold 4% Long-term compliance requirements Executed this month (blue) Output: Nash equilibrium decision path: 1. Immediate implementation: Power grid companies: Replace quintuple harmonic filter modules by March 25, 2025 (cost: 1.2 million yuan); Regulatory agencies: Implement a 4% threshold for harmonic distortion starting March 21, 2025; 2. This week's implementation: Users: Sign electricity contract adjustment agreements by March 28, 2025 (±10% load, cost: 45,000 yuan / month); 3. This month's implementation: Regulatory agencies: Upgrade abnormal transaction audit algorithms by April 10, 2025 (intensity +20%). This path prioritizes high-risk harmonic issues, balancing equipment replacement costs (1.2 million ≤ 2 million budget), user acceptance (adjustment cost 45,000 ≤ 50,000) and regulatory compliance (threshold adjustment 1% to match risk level), achieving Pareto optimality.

[0046] 205. Based on the Nash equilibrium decision path, the system is converted into a spin glass system Hamiltonian, where each spin represents an asset allocation decision (equipment replacement, budget allocation), and the strength of the spin interaction is determined by the economic correlation between the decisions. A simulated annealing algorithm is used to solve the system ground state and output an anti-interference asset allocation table, including: equipment replacement time sequence (accurate to weekly granularity), maintenance cost sharing ratio (grid company, user, supplier ratio), and regulatory compliance verification label (compliant / pending correction / violation). Specifically, the Hamiltonian of the spin glass system is constructed: each strategy node in the Nash equilibrium decision path is mapped to a spin direction: spin up represents the execution of the asset allocation decision (equipment replacement); spin down represents the suspension of execution; the spin interaction strength is defined according to the economic correlation between the 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 definition and interaction matrix); simulated annealing ground state solution: initialize the spin system temperature to 1000K, and gradually cool it to 1K according to the exponential cooling strategy; update the spin direction at each temperature point through Monte Carlo sampling until the system energy converges; output product: ground state spin distribution diagram (marking the final direction and energy value of all spins); asset allocation time series analysis: sort the spin-up nodes in the ground state spin distribution diagram according to the following rules: the corresponding strategies for high-risk periods are executed first (from the Z-axis risk level of the quantum risk heat map); the critical failure time in the equipment health decay curve is used as the key failure time in the equipment health decay curve. The output product is a hard deadline; the output product is an equipment replacement time sequence list (accurate to weekly granularity, including execution priority tags); cost sharing ratio calculation: the execution ratio of relevant strategies of power grid enterprises, users, and suppliers in the statistical ground state spin distribution; the total maintenance cost is proportionally allocated, satisfying: the power grid enterprise sharing ratio ∈ [40%, 70%] (dynamically adjusted according to the equipment ownership ratio); the user sharing ratio ∈ [10%, 30%] (based on the energy efficiency reward and penalty clauses in the electricity contract); the supplier sharing ratio ∈ [5%, 15%] (based on the terms of the equipment warranty agreement); the output product is a dynamic cost sharing ratio table (including the proportions of the three parties and the basis for adjustment); the compliance verification tag is dynamically generated: the equipment replacement time sequence list is matched with the terms in the power market regulatory rule library: compliance: the execution time of all strategies is earlier than the latest deadline required by the regulation; pending correction: the execution time of at least one strategy exceeds the deadline but does not reach the penalty threshold; violation: the execution time of key strategies exceeds the regulatory penalty red line; the final product is an anti-interference asset allocation table (including the time sequence list, sharing ratio and compliance tag), which serves as the only input source for generating quantum anti-counterfeiting regulatory reports.

[0047] It should be noted that the following is an example of a spin glass system optimization for a power grid company based on the Nash equilibrium decision path on March 20, 2025: The Hamiltonian of the spin glass system is constructed. Input data: the set of strategies in the Nash equilibrium decision path: Strategy 1: Replace the quintuple harmonic filter module before March 25, 2025 (cost: 1.2 million yuan); Strategy 2: User load adjustment: ±10% (cost: 45,000 yuan / month); Strategy 3: Regulatory threshold: 4% (audit cost + 20%). Spin mapping rules: Policy Node Spin direction definition Economic correlation analysis Strategy 1 (Equipment Replacement) Spin Up (Execute) There is a synergistic effect with Strategy 3 (reducing compliance risk) → interaction strength J = -0.5 Strategy 2 (User Adjustment) Spin Up (Execute) There is resource competition with strategy 1 (budget conflict) → J = +0.3 Strategy 3 (Regulatory Adjustment) Spin Down (on hold) Cooperate with strategy 1, have nothing to do with strategy 2 → J = 0 Hamiltonian parameter table: spin nodes: 1 (strategy 1), 2 (strategy 2), 3 (strategy 3); Interaction matrix: J(1,2)=+0.3,J(1,3)=-0.5,J(2,3)=0; The simulated annealing ground state solution was performed, with the following annealing parameters: initial temperature = 1000 K, exponential cooling coefficient = 0.95, and number of annealing iterations = 1000; Monte Carlo sampling: At temperature T=10K, the system energy converges to the lowest state -1.2eV.

[0048] Ground state spin distribution: Spin Node Final Direction Energy value (eV) 1 ↑ -0.8 2 ↓ -0.3 3 ↑ -0.1 Asset allocation timing analysis and execution priority rules: Strategy 1 (high-risk period association) → immediate execution (before March 25, 2025); Strategy 3 (no hard deadline) → second priority execution (before April 10, 2025); Strategy 2 (user adjustment) → suspended execution due to downward spin; Equipment replacement time list: Device Type Execution time window Priority Label 5th harmonic filter module 2025-03-25 (Week 12) Red (immediately) Regulatory system upgrade 2025-04-10 (Week 15) Orange (this week) Cost sharing ratio calculation, strategy execution ratio: Number of grid enterprise-related strategy executions: 2 (Strategy 1, Strategy 3); Number of user-related strategy executions: 0; Number of supplier-related strategy executions: 0; Apportionment rules apply: main body Calculation of apportionment ratio Amount of apportionment (total maintenance cost 2 million) Power grid companies 70% (upper limit of ownership ratio) 1.4 million user 10% (lower limit of energy efficiency reward and penalty clauses) 200,000 supplier 15% (guarantee agreement upper limit) 300,000 Compliance verification tag generation and regulatory rule matching: Strategy execution time Regulatory deadline Compliance determination 5th harmonic module replacement 2025-04-01 Comply (√) Regulatory system upgrade 2025-04-30 To be corrected (Δ) Output product: Anti-interference asset allocation table: 1. Equipment replacement schedule: March 25, 2025: Replacement of the 5th harmonic filter module (cost: 1.2 million yuan); April 10, 2025: Upgrade of the monitoring system (cost: 300,000 yuan); 2. Maintenance cost sharing ratio: Power grid companies: 1.4 million (70%); users: 200,000 (10%); suppliers: 300,000 (15%); 3. Compliance Verification Label: 5th Harmonic Replacement: Compliant (√); Regulatory Upgrade: Pending Revision (Δ) (implemented 10 days later than the standard deadline); This configuration table eliminates user load adjustment strategies (due to resource conflicts) through the spin glass model, prioritizes the replacement of high-risk equipment, and dynamically allocates costs (grid companies bear 70%). It also marks items to be corrected for regulatory upgrades and provides structured input for quantum anti-counterfeiting reports.

[0049] 206. Encode the anti-interference asset allocation table as a photon orbital angular momentum state, and implement data topology protection through an optical ring cavity; generate a quantum anti-counterfeiting regulatory report, including: Harmonic Governance Cost-Ratio (CBR): Calculate the power quality improvement value per unit cost based on the harmonic economic impact factor matrix; Dynamic Electricity Price Strategy Compliance Index: Compare the deviation between the real-time electricity price and the Nash equilibrium solution; Multi-party Responsibility Traceability Chain: Record the decision-making contribution weight of each subject through the photon orbital angular momentum entangled state; Specifically, photon orbital angular momentum encoding: convert the anti-interference asset configuration table in step 5 into a binary data stream, and map each bit of data into a photon orbital angular momentum state (OAM state) through a spatial light modulator; define the OAM topology encoding rules: the OAM order represents the data field type (the equipment replacement timing is +5 orders, and the cost sharing ratio is -3 orders); the polarization direction represents the data version identifier (left-hand rotation is the current version, right-hand rotation is the historical version); the output product: topologically protected photon data stream (containing dual encoding information of OAM state and polarization state); calculation of harmonic governance efficiency-cost ratio (CBR): extract the unit time power quality loss cost from the harmonic economic impact factor matrix (HEIF matrix); combined with the maintenance cost sharing ratio, calculate the CBR value: CBR=total value of power quality improvement / maintenance Total cost of protection; Output: Dynamic CBR curve (sequence of efficiency-cost ratios updated at weekly granularity); Dynamic electricity price compliance index generation: Obtain real-time electricity price data from the power market trading platform and compare it with the theoretical electricity price in the Nash equilibrium decision path; Calculate the deviation index: Compliance index = 1 - |actual electricity price - equilibrium electricity price| / equilibrium electricity price; Output: Electricity price compliance index table (including timestamp, deviation, and risk level label); Multi-party responsibility traceability chain construction: Encode the execution records of the three-party game strategy into photon entangled state pairs: Each entity (grid enterprise, 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: Optical topological responsibility traceability chain (the decision contribution of each entity can be restored through interferometer measurement); Quantum anti-counterfeiting report synthesis and verification: input the topologically protected photon data stream, dynamic CBR curve, electricity price compliance index table and optical topology responsibility traceability chain into the report synthesizer; realize irreversible data binding through the optical ring cavity to generate a machine-readable regulatory report, which includes: non-tamperable feature: OAM topology encoding check bit; visualization module: risk heat map superimposed on CBR curve; audit interface: support regulatory agency API to retrieve photon raw data for topology verification; final product: quantum anti-counterfeiting regulatory report (output optical media version and digital signature version at the same time).

[0050] It should be noted that the following is an example of a power grid company generating a quantum anti-counterfeiting regulatory report based on the anti-interference asset configuration table on March 25, 2025: Photon orbital angular momentum (OAM) encoding, input data: Anti-interference asset allocation table (part): Equipment replacement schedule: 2025-03-25, replacement of the 5th harmonic module (APF-10kV / 500A); Maintenance cost sharing ratio: Grid company 70% (1.4 million RMB), user 10% (200,000 RMB), supplier 15% (300,000 RMB); Compliance label: Equipment replacement complies (√), regulatory upgrade pending revision (Δ); OAM encoding rules: Data Field 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 Label +1 right-handed 01 (Compliant / To be revised) Output products: Topologically protected photon data stream (partial): Photon 1: OAM+5, left-handed polarization, encoding 11010011; Photon 2: OAM-3, left-handed polarization, encoding 1010; Photon 3: OAM+1, right-handed polarization, encoding 01; Calculation of harmonic control cost-to-performance ratio (CBR): Input data: HEIF matrix (2025-03-20 to 03-25): Total cost reduction of power quality loss = 5 million yuan; Total maintenance cost = 2 million yuan (1.4 million yuan for the grid + 200,000 yuan for users + 300,000 yuan for suppliers + 100,000 yuan for others); CBR calculation: CBR = 2 million yuan / 5 million yuan = 2.5; this means that for every 1 yuan invested in maintenance costs, the power quality benefit is improved by 2.5 yuan.

[0051] Output product: Dynamic CBR curve (weekly granularity): 2025-03-25: CBR=2.5 (peak performance); 2025-03-18: CBR=1.8; 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); Deviation calculation: Deviation = ; Compliance index = 1 − 0.0333 = 0.9667 (96.67%); Output: Electricity Price Compliance Index Table: Timestamp Deviation Compliance Index Risk Level 2025-03-25T10:00 3.33% 96.67% Low risk Multi-party responsibility traceability chain construction and policy execution record coding: main body OAM entangled state identification Strategy contribution weight (association strength) Power grid companies OAM+8th order 0.8 (leading equipment replacement decisions) user OAM+2nd order 0.5 (partial acceptance load adjustment) Regulatory agencies OAM-5th order 0.6 (threshold adjustment contribution) Interferometer measurement results: The three-party strategy correlation strength is: power grid enterprise → regulatory agency = 0.75, user → power grid enterprise = 0.3.

[0052] Quantum anti-counterfeiting report synthesis and verification, data binding and output: tamper-proof features: optical ring cavity generates hash fingerprints, visualization modules, audit interfaces; Final product: Quantum Anti-Counterfeiting Regulatory Report (2025-03-25 version): Optical media version: burned on a quantum storage disc, with an OAM topology check bit matching rate of 99.99%; Digital signature version: PDF file (digital summary: x509-SHA256); This report uses photon encoding and quantum entanglement technology to irreversibly link asset allocation, CBR effectiveness, electricity price compliance, and accountability, providing regulators with a verifiable and tamper-proof basis for decision-making. The power grid company's equipment replacement strategy contributes a 0.8% weight, consistent with its 70% cost-sharing ratio, validating the reliability of the data topology.

[0053] In the embodiments of the present invention, real-time data collection and processing are used to ensure the accuracy and timeliness of the evaluation results. The application of quantum computing and chaos testing technology further improves the precision and sensitivity of the evaluation. The output of quantum risk heat maps and equipment health decay curves provides a scientific decision-making basis for power grid companies. The optimized asset allocation plan of the Nash equilibrium decision path balances equipment replacement costs, user electricity costs and regulatory compliance, thereby improving the economy and reliability of the power grid. The application of three-party game trees and Nash equilibrium solution technology takes into account the interests of power grid companies, users and regulatory agencies. Through the policy 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 a verifiable and tamper-proof decision-making basis for regulatory agencies, enhancing the transparency and credibility of supervision. The simulated annealing algorithm is used to solve the ground state of the spin glass system, output the optimal asset allocation plan, dynamic cost sharing ratio table and regulatory compliance verification label, ensuring the rationality of resource allocation and the reduction of maintenance costs. In summary, this technology achieves comprehensive and dynamic assessment and optimization of the power grid status through the comprehensive use of multiple advanced technical means, improves the economy, reliability and safety of the power grid, provides a scientific basis for the decision-making of power grid companies, and promotes win-win results for multiple stakeholders.

[0054] The above describes the grid state assessment method based on a large-capacity high-voltage active filter structure in an embodiment of the present invention. The following describes the grid state assessment device based on a large-capacity high-voltage active filter structure in an embodiment of the present invention. Figure 3In one embodiment of the present invention, a power grid state assessment device based on a large-capacity high-voltage active filter structure includes: an acquisition module 301, which is used to obtain the harmonic suppression rate of each harmonic, 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 the time window; a processing module 302, which is used to map the harmonic economic impact factor matrix into a quantum bit topology network, solve the network's lowest energy state through a quantum annealing processor, identify high-energy nodes and output a quantum risk heat map; an assessment module 303, which is used to inject a chaotic test signal into the active filter, collect the voltage response signal at the output end, calculate the Lyapunov exponent spectrum of the voltage response signal, and screen the maximum positive exponent The value is used as the criterion for device stability, and the device health attenuation curve is output; the decision module 304 is used to construct a three-party game tree of power grid enterprises, users and regulatory agencies based on the quantum risk heat map and the device health attenuation curve, solve the Nash equilibrium point by reverse induction, and output the Nash equilibrium decision path; the configuration module 305 is used to convert the Nash equilibrium decision path into the Hamiltonian of the spin glass system, use the simulated annealing algorithm to solve the system ground state, and output the anti-interference asset allocation table; the reporting module 306 is used to encode the anti-interference asset allocation table into the photon orbital angular momentum state, realize data topology protection through the optical ring cavity, and generate a quantum anti-counterfeiting supervision report, which includes the harmonic governance efficiency-cost ratio, the dynamic electricity price strategy compliance index and the multi-party responsibility traceability chain.

[0055] In the embodiment of the present invention, by integrating multiple functional modules, a comprehensive and dynamic evaluation and optimization of the power grid status is achieved, the economy, stability and security of the power grid are improved, a scientific basis is provided for the decision-making of power grid enterprises, and cooperation and win-win results are promoted among multiple stakeholders.

[0056] above Figure 3 The power grid state assessment device based on a large-capacity high-voltage active filter structure in an embodiment of the present invention is described in detail from the perspective of modular functional entities. The power grid state assessment device based on a large-capacity high-voltage active filter structure in an embodiment of the present invention is described in detail from the perspective of hardware processing.

[0057] Figure 4This is a schematic diagram of the structure of a power grid state assessment device based on a large-capacity, high-voltage active filter structure, provided by an embodiment of the present invention. This power grid state assessment device 400 based on a large-capacity, high-voltage active filter structure can vary significantly depending on configuration or performance. It may include one or more processors (central processing units, CPUs) 410 (e.g., one or more processors), 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 medium 430 may be either transient or persistent storage. The program stored in the storage medium 430 may include one or more modules (not shown), each of which may include a series of instructions for operating on the power grid state assessment device 400 based on a large-capacity, high-voltage active filter structure. Furthermore, the processor 410 may be configured to communicate with the storage medium 430 to execute the series of instructions stored in the storage medium 430 on the power grid state assessment device 400 based on a large-capacity, high-voltage active filter structure.

[0058] The grid state assessment device 400 based on a large-capacity high-voltage active filter structure may further include one or more power supplies 440, one or more wired or wireless network interfaces 450, one or more input and output interfaces 460, and / or one or more operating systems 431, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD, etc. It will be understood by those skilled in the art that Figure 4 The structure of the grid state assessment device based on the large-capacity high-voltage active filtering structure shown does not constitute a limitation on the grid state assessment device based on the large-capacity high-voltage active filtering structure, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0059] The present invention also provides a power grid state assessment device based on a large-capacity high-voltage active filter structure. The power grid state assessment device based on a large-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 executes the steps of the power grid state assessment method based on the large-capacity high-voltage active filter structure in the above-mentioned embodiments.

[0060] The present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. The computer-readable storage medium stores instructions. When the instructions are executed on a computer, the computer executes the steps of the power grid status assessment method based on a large-capacity high-voltage active filter structure.

[0061] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0062] 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, or the portion 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 for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0063] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that the technical solutions described in the above embodiments can still be modified, or some of the technical features thereof can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A grid state assessment method based on a large-capacity high-voltage active filter structure, characterized in that: The grid state assessment method based on a large-capacity high-voltage active filter structure includes: Obtain the harmonic suppression rate, equipment energy consumption data, and maintenance cost records of large-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 based on the time window; Mapping the harmonic economic impact factor matrix into a quantum bit topology network, solving the lowest energy state of the network through a quantum annealing processor, identifying high-energy nodes and outputting a quantum risk heat map; Inject a chaotic test signal into the active filter, collect the voltage response signal at the output, 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 attenuation curve; Based on the quantum risk heat map and the equipment health decay curve, a three-party game tree is constructed among the power grid company, the user, and the regulatory agency. The Nash equilibrium point is solved by the reverse induction method, 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 simulated annealing algorithm is used to solve the system ground state, and an anti-interference asset allocation table is output; The anti-interference asset allocation table is encoded into the 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, the dynamic electricity price strategy compliance index and the multi-party responsibility traceability chain.

2. The grid state assessment method based on a large-capacity high-voltage active filter structure according to claim 1 is characterized in that: include: Collect the harmonic suppression rate of each active filter, real-time electricity price fluctuation data of grid nodes, equipment maintenance cost records and life cycle history data to obtain the original data stream with time stamp alignment; According to the electricity market trading rules of the area where the grid node is located, the sensitivity coefficient of the real-time electricity price to harmonic disturbances is calculated to obtain a dynamic electricity price sensitivity coefficient table; Based on the accumulated operating time and historical maintenance records of the active filter, an equipment life attenuation model is constructed to obtain the equipment life attenuation weight vector; The original data stream, sensitivity coefficient table, and attenuation weight vector are input into the matrix generator. The matrix data integrity is verified by the optical topology checker, and a digital fingerprint with a timestamp is generated to obtain a verifiable harmonic economic impact factor matrix.

3. The grid state assessment method based on a large-capacity high-voltage active filter structure according to claim 1 is characterized in that: include: The harmonic economic impact factor matrix is ​​mapped to quantum bit nodes, and the matrix element values ​​are converted into the coupling strength between quantum bits. The initial state of the quantum bit is defined as the direction of the superconducting ring current. Clockwise represents low risk, and counterclockwise represents high risk. This results in a quantum coupling network topology diagram. The quantum coupling network topology is input into the quantum annealing processor. A transverse magnetic field is applied and the magnetic field strength is gradually reduced. The system is converged 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. Extract the energy value from the quantum state distribution table after annealing to obtain a three-dimensional risk level mapping table; The three-dimensional risk level mapping table is encoded into 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 to obtain an interactive quantum risk heat map.

4. The grid state assessment method based on a large-capacity high-voltage active filter structure according to claim 1 is characterized in that: include: A test signal with chaotic characteristics is generated based on the Lorentz equations. The test signal is injected into the main circuit of the active filter through an isolation transformer to obtain a chaotic response data set. The output signal in the chaotic response data set is reconstructed in phase space, and its maximum Lyapunov exponent λmax is calculated. When λmax>0, the device is judged to be at risk of dynamic instability. The absolute value of the exponent is inversely proportional to the health of the device. A table of device stability criteria is obtained. A baseline health curve is established based on the historical maintenance records of the equipment, using a two-parameter exponential decay model: Health score H(t)=H0×e^(-αt)+β×maintenance times; Among them, α is dynamically modified by λmax, and β is determined by the quantitative value of the maintenance effect; Get the dynamic attenuation model parameter set; The dynamic attenuation model parameters are input into the failure time predictor to solve the time point when the health score first falls below the safety threshold, and the equipment health attenuation curve is obtained.

5. The grid state assessment method based on a large-capacity high-voltage active filter structure according to claim 1 is characterized in that: include: Based on the quantum risk heat map, high-risk time periods and frequency bands are extracted to generate equipment replacement budget allocation constraints for power grid companies. Based on the key failure time nodes in the equipment health decay curve, 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 library as the regulatory agency's policy baseline. Get the tripartite strategy space table; The risk level in the quantum risk heat map is converted into the economic loss weight of the power grid enterprise, the equipment health score is mapped to the cost sensitivity coefficient of the user's electricity contract adjustment, and the positive correlation between the regulatory agency's compliance threshold adjustment step and the risk level is defined; and the game tree node parameter mapping table is obtained; Tracing back from the end node of the game tree, the utility function value of each strategy combination is calculated layer by layer: the utility of the power grid enterprise is the reduction in equipment maintenance costs + the benefits of improved power supply quality; the utility of the user is the reduction in electricity bill expenditure - the cost of adjusting the electricity contract; the utility of the regulatory agency is the improvement in compliance rate - the audit cost. The candidate equilibrium strategy set is obtained. From the candidate equilibrium strategy set, we select solutions that meet the following conditions: the power grid enterprise's equipment replacement cost ≤ the budget constraint; the user's electricity contract adjustment range ∈ the energy efficiency reward and punishment acceptance threshold; the regulatory compliance threshold adjustment step size matches the risk level; and obtain the Pareto optimal solution set. According to the time window distribution of the quantum risk heat map, the strategies in the Pareto optimal solution set are sorted by execution urgency to obtain the Nash equilibrium decision path.

6. The grid state assessment method based on a large-capacity high-voltage active filter structure according to claim 1 is characterized in that: include: Map each strategy node in the Nash equilibrium decision path to a spin direction: spin-up means executing the asset allocation decision, and spin-down means postponing the execution; The spin interaction strength is defined according to the economic correlation between strategies, and the spin Hamiltonian parameter table is obtained; At each temperature point, the spin direction is updated through Monte Carlo sampling until the system energy converges, and the ground state spin distribution diagram is obtained, marking the final direction and energy value of all spins; The nodes with spin-up in the ground-state spin distribution graph are sorted according to the following rules: the strategies corresponding to high-risk periods are executed first, and the critical failure time in the equipment health decay curve is used as a hard deadline to obtain a time-series list for equipment replacement. The execution ratio of relevant strategies of power grid enterprises, users and suppliers in the base state spin distribution is calculated, and the total maintenance cost is allocated proportionally to obtain a dynamic cost allocation ratio table; The equipment replacement time sequence list is matched with the clauses in the power market regulatory rule base to obtain the anti-interference asset configuration table.

7. The grid state assessment method based on a large-capacity high-voltage active filter structure according to claim 1 is characterized in that: include: Convert the anti-interference asset allocation table into a binary data stream, and use a spatial light modulator to map each bit of data into a photon orbital angular momentum state to obtain a topologically protected photon data stream; Extract the power quality loss cost per unit time from the harmonic economic impact factor matrix, and calculate the CBR value based on the maintenance cost allocation ratio: CBR = total value of power quality improvement / total maintenance cost; Get the dynamic CBR curve; Obtain real-time electricity price data from the power market trading platform and compare it with the theoretical electricity price in the Nash equilibrium decision path: compliance index = 1-|actual electricity price-equilibrium electricity price| / equilibrium electricity price; Get 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. The strategy contribution weight is quantified by the correlation strength of the entangled photon pairs, and an optical topological responsibility traceability chain is obtained. The topologically protected photon data stream, dynamic CBR curve, electricity price compliance index table and optical topological responsibility traceability chain are input into the report synthesizer, and the data is irreversibly bound through the optical ring cavity to generate a machine-readable regulatory report to obtain a quantum anti-counterfeiting regulatory report.

8. A power grid status assessment device based on a large-capacity high-voltage active filter structure, characterized in that: The power grid state assessment device based on a large-capacity high-voltage active filter structure includes: The acquisition module is used to obtain the harmonic suppression rate, equipment energy consumption data and maintenance cost records of large-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 based on the time window; A processing module is used to map the harmonic economic impact factor matrix into a quantum bit topology network, solve the lowest 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 a chaotic test signal into the active filter, collect the voltage response signal at the output, 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 attenuation curve; A decision-making module is used to construct a three-party game tree among the power grid company, the user, and the regulatory agency based on the quantum risk heat map and the device health decay curve, solve the Nash equilibrium point through reverse 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, use the simulated annealing algorithm to solve the system ground state, and output the anti-interference asset configuration table; The reporting module is used to encode the anti-interference asset allocation table into the photon orbital angular momentum state, realize data topology protection through the optical ring cavity, and generate a quantum anti-counterfeiting supervision report. The quantum anti-counterfeiting supervision report includes the harmonic governance efficiency-cost ratio, the dynamic electricity price strategy compliance index and the multi-party responsibility traceability chain.

9. A power grid status assessment device based on a large-capacity high-voltage active filter structure, characterized in that: The power grid state assessment device based on a large-capacity high-voltage active filter structure includes: a memory and at least one processor, wherein instructions are stored in the memory; The at least one processor calls the instructions in the memory to enable the power grid state assessment device based on a large-capacity high-voltage active filter structure to execute the power grid state assessment method based on a large-capacity high-voltage active filter structure according to any one of claims 1 to 7.

10. A computer-readable storage medium having instructions stored thereon, characterized in that: When the instructions are executed by the processor, the grid state assessment method based on a large-capacity high-voltage active filter structure according to any one of claims 1 to 7 is implemented.

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