A new energy efficient dispatching method and system based on smart grid

Abstract

The present application belongs to the field of smart grid, and discloses a new energy efficient scheduling method and system based on smart grid; including embedding diamond NV color center micro sensor to form networked array, real-time collection and output of original data, extraction of auxiliary environmental variables for correction, generation of weight vector, layer-by-layer adjustment of fluorescence intensity sequence, extraction of projection value iterative optimization, and output of harmonic phase matrix; decomposition of harmonic phase matrix, extraction of coupling path characteristics, calculation of entanglement entropy, simulation of storm evolution if exceeding threshold, output of alarm package, otherwise continue to cycle; reconstruction of topology structure based on alarm package, injection of chaotic anti-phase signal, verification of stability and output of configuration summary; receiving configuration summary running pulse neural network, generating instructions based on optical topology scheduling strategy, encrypting and distributing instructions, releasing compensation amount to complete execution, and realizing new energy efficient scheduling based on smart grid.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of smart grid, more particularly, the present application relates to a new energy efficient scheduling method and system based on smart grid. BACKGROUND

[0002] When scheduling power energy in the city demand peak period, harmonic pollution is caused by the multi-source interaction of solar or wind inverter and household appliances. This pollution is not the traditional low-frequency harmonic (usually in the range of 50-2500Hz, caused by industrial load), but is concentrated in the medium-high frequency band of 2-9kHz, and its generation mechanism is highly dependent on the dynamic coupling of the nonlinear characteristics of distributed new energy equipment and the dense consumption load in the city, making it difficult for the existing scheduling system to effectively suppress it through standard filters (such as passive LC filters).

[0003] Solar panels or wind turbines convert direct current into alternating current through inverters and connect to the smart grid. These inverters use pulse width modulation (PWM) technology, and when switching high loads, high-frequency switching noise is generated. This noise appears as a sharp harmonic in the 2-9kHz range, especially in urban environments where multiple inverters are deployed in clusters, and when multiple inverters operate synchronously, the harmonic signal is amplified and forms a resonance effect.

[0004] Therefore, harmonic pollution has an impact on the efficiency and stability of the power grid, such as causing a decrease in power factor and an increase in reactive power consumption, especially during peak periods, which means that the originally available power is greatly wasted due to harmonic loss.

[0005] Therefore, new energy efficient scheduling based on smart grid needs to be designed and innovated to meet actual needs. SUMMARY

[0006] In order to overcome the above-mentioned defects of the prior art, in order to achieve the above-mentioned purpose, the present application provides the following technical scheme: a new energy efficient scheduling method based on smart grid, comprising:

[0007] S1: embedding a diamond NV color center micro sensor in the DC or AC port of the inverter in the new energy power grid to form a networked array, collecting and outputting raw data in real time;

[0008] S2: extracting auxiliary environmental variables, correcting based on temperature, inverter power and load input current, generating a weight vector, adjusting the fluorescence intensity sequence layer by layer, and forming corrected data;

[0009] S3: selecting a measurement basis to map to a quantum Hilbert space, forming an initial rough matrix, extracting a projection value for iterative optimization, and outputting a harmonic phase matrix;

[0010] S4: decompose the harmonic phase matrix, extract the coupling path feature, calculate the entanglement entropy, if it exceeds the entropy threshold, simulate the storm evolution, output the alarm package, otherwise continue the loop;

[0011] S5: reconstruct the topology based on the alarm package, inject the chaotic anti-phase signal, judge whether to apply to the new energy power grid through stability verification, and output the configuration summary;

[0012] S6: receive the configuration summary to run the pulse neural network, generate a scheduling instruction based on the optical topology scheduling strategy, verify and output the scheduling instruction and performance summary;

[0013] S7: encrypt and distribute the scheduling instruction to the new energy node, then submit the harmonic suppression proof for consensus verification, release the compensation amount to complete the execution.

[0014] Preferably, the method for collecting and outputting raw data in real time comprises:

[0015] Embedding a diamond NV color center micro sensor in the DC or AC port of the inverter to form a networked array, each node equipped with the sensor;

[0016] When the sensor is working in real time, first use continuous laser pumping NV center to make it enter the excited state; then apply a microwave pulse to detect local magnetic field or electric field changes, and convert the detected changes into a fluorescence signal, then convert it into a digital signal through a photodetector, with a sampling rate greater than 100kHz; the digital signal is stored in the form of time series;

[0017] Output the raw data, which includes the fluorescence intensity time series, the phase offset vector, the amplitude spectrum, the auxiliary environmental variables, and the time stamp and node position.

[0018] Preferably, the S2 comprises:

[0019] Extract all relevant auxiliary environmental variables from the raw data, including temperature, inverter power and load input current, and align the time stamp with the fluorescence intensity time series;

[0020] Then, detect whether the temperature exceeds the temperature threshold, if so, increase the correction weight of the inverter power and the load current, if not, keep the default weight; then, detect whether the load input current is higher than the average value and the inverter power exceeds the expectation, if so, mark it as a high interaction scenario, increase the joint weight of the current and the power, otherwise, keep the weight processed in the last step, then generate a weight vector;

[0021] Based on the weight vector, the fluorescence intensity time series is corrected. First, the temperature is used to calculate the basic deviation, specifically, if the temperature is higher than the reference value, the fluorescence intensity is amplified; if the temperature is lower than the reference value, the fluorescence intensity is reduced, if the temperature exceeds the temperature threshold, the fluorescence intensity is additionally increased on the basis of adjustment based on the reference value, and the preliminary corrected fluorescence intensity time series is output;

[0022] Then, the power level is used to calculate the deviation, if the inverter power is higher than the median value, the fluorescence intensity is amplified; if the inverter power is lower than the median value, the fluorescence intensity is reduced; if the temperature exceeds the temperature threshold, the amplification effect of power correction is increased; if the load input current is higher than the average value, the fluorescence intensity is further reduced on the basis of adjustment based on the median value and the temperature, and the updated fluorescence intensity time series is output.

[0023] Finally, the current value is subtracted by the pulse offset, and the fluorescence intensity is scaled according to the proportion of the current relative to the average value, if the high interaction scene is marked, the scaling effect of current correction is increased; if the temperature exceeds the temperature threshold, the offset subtraction is reduced, and the completely integrated fluorescence intensity time series is output, and the original data is updated to form the corrected data.

[0024] Preferably, S3 and S4 comprise:

[0025] For the corrected data, a measurement basis is selected, the fluorescence intensity time series is mapped to the quantum Hilbert space, an initial coarse matrix is formed, projection values are extracted from the phase offset vector and the amplitude spectrum, and the initial coarse matrix is iteratively optimized to match the extracted projection values, so that the matrix is positive definite and the trace is 1, and a partial trace operation is used to remove irrelevant subspaces, and a final harmonic phase matrix is output.

[0026] The harmonic phase matrix is decomposed into left singular vectors, singular values and a diagonal matrix, wherein the singular values represent characteristic intensities, the coupling paths are extracted from the left singular vectors, the frequency modes and the phase offsets are calculated in combination with the amplitude spectrum, and the auxiliary environmental variables are used for weighting, filtering of secondary characteristics, and generation of a coupling path characteristic list.

[0027] The entanglement entropy is calculated according to the harmonic phase matrix, the entropy value of the entanglement entropy is compared with the entropy threshold value, if the entanglement entropy value exceeds the entropy threshold value, the storm evolution is simulated in combination with the coupling path characteristics, an alarm package is output, including the prediction time, the storm probability, the coupling path characteristics, the harmonic phase matrix summary, and the time stamp and node position, otherwise, no output, and the cycle continues.

[0028] Preferably, S5 comprises:

[0029] The silicon-based photonic crystal component is installed on the critical coupling path, connected with the quantum harmonic pre-diagnosis layer through the electric control interface, receives the alarm package, sets the voltage applied by the electric control liquid crystal super surface based on the coupling path characteristics and storm probability, and reconstructs the topological structure; based on the load adaptive adjustment parameter, generates and injects the chaotic anti-phase signal; verifies the synchronization stability, if stable, applies to the power grid, otherwise, returns to reconstruction and adjustment of the forbidden band range; outputs the reconstructed topological configuration and chaotic anti-phase signal summary, with time stamp and node position;

[0030] The topological configuration includes the reconstructed topological structure and the forbidden band parameter, and the chaotic anti-phase signal summary includes the generation parameter and the cancellation efficiency.

[0031] Preferably, the method of setting the voltage applied by the electric control liquid crystal super surface based on the coupling path characteristics and storm probability, and reconstructing the topological structure comprises:

[0032] Extract the coupling path characteristics and storm probability in the alarm package, set the initial topological structure as linear, which is represented as linear forbidden band, if the coupling path frequency is less than 3kHz and the storm probability is greater than the first limit value, adjust the voltage to 4V, adjust the topological structure to spiral, form spiral forbidden band, if the coupling path frequency shows 3-5kHz and the storm probability is greater than the second limit value, adjust the voltage to 5V, adjust the topological structure to ring, form ring forbidden band; if the coupling path frequency shows 5-7kHz and the storm probability is greater than the third limit value, adjust the voltage to 6V, adjust the topological structure to grid, form grid forbidden band, if the coupling path frequency shows 7-9kHz and the storm probability is greater than the fourth limit value, adjust the voltage to 7V, adjust the topological structure to star, form star forbidden band; if the coupling path shows resonance energy dispersion demand and the storm probability is greater than the fifth limit value, adjust the voltage to 8V, adjust the topological structure to fractal, form fractal forbidden band; if the coupling path shows multi-path superposition and the storm probability is less than or equal to the second limit value, keep the linear topological structure and fine-tune the voltage to 3V.

[0033] Preferably, the S6 comprises:

[0034] Pre-train the pulse neural network model and embed it in the dispatch center, connect the inverter and the storage center to form a distributed control network, connect the optical topology reconstruction layer through the interface, run the pulse neural network model based on the topological configuration and storm probability, and dynamically respond to specific scenarios based on the optical topology scheduling strategy, generate scheduling instructions, verify the stability of the scheduling instructions, if stable, apply to the power grid, otherwise, return to update; output the optimized scheduling instructions and performance summary.

[0035] Preferably, the method that the light topology scheduling strategy dynamically responds to specific scenes comprises: extracting a topology configuration and a storm probability, setting an initial light topology scheduling strategy as a default linear distribution, representing uniform power output, if the topology configuration shows a ring-shaped forbidden band and the storm probability is greater than a first probability threshold, adjusting the light topology scheduling strategy to a isolation priority strategy, reducing inverter output;If the topology configuration shows a star-shaped forbidden band and the storm probability is greater than a second probability threshold, adjust to a block priority strategy, preferentially reserve new energy compensation;If the topology configuration shows a fractal forbidden band and the storm probability is greater than a third probability threshold, adjust to a dispersion priority strategy, disperse resonant energy;If the topology configuration shows a grid forbidden band and the storm probability is less than or equal to the first probability threshold, keep the default linear distribution.

[0036] Preferably, the scheduling instruction is encrypted and distributed to the new energy node by the scheduling center, the new energy node submits a harmonic suppression proof to the block chain, the block chain releases a compensation amount to the demand side load node based on the harmonic suppression proof, and the smart contract is automatically executed based on the harmonic suppression proof.

[0037] A new energy efficient scheduling system based on a smart grid, comprising:

[0038] The quantum sensing module: embedded diamond NV color center micro sensor collects raw data, performs fluorescence intensity correction and harmonic phase matrix reconstruction, and outputs an alarm package;

[0039] The light topology reconstruction module: receives the alarm package to reconstruct the topology structure, injects a chaotic anti-phase signal, and outputs a configuration summary;

[0040] The dynamic scheduling module: based on the configuration summary, generate scheduling instructions, respond to specific scenes to optimize distribution, and output scheduling instructions;

[0041] The block chain consensus module: encrypt and distribute the instructions, verify the harmonic suppression proof, execute compensation, and form a closed loop.

[0042] The technical effects and advantages of the new energy efficient scheduling method based on a smart grid of the application are as follows:

[0043] By embedding a networked array of diamond NV color center micro sensors, fine-grained detection of 2-9kHz medium-high frequency peak harmonics is realized, non-static variations and multi-source coupling paths are captured, high-resolution data basis is provided for subsequent correction and prediction, early signs of "harmonic storm" are effectively identified, and efficiency loss and equipment overheating risk caused by hidden noise are reduced.

[0044] The innovative weight vector mechanism dynamically processes variable interaction, accurately corrects fluorescence intensity deviation caused by uneven peak load and multi-source interaction, reduces temperature-induced false peak and power or current pulse offset, ensures that the data reflects the true harmonic variation, provides pure input for downstream matrix reconstruction, and improves the prediction accuracy of the overall system to the "harmonic storm".

[0045] Quantum state tomography and entanglement entropy calculation innovatively quantify quantum correlation, reduce equipment failure rate, and realize early warning through the alarm package mechanism, reduce maintenance cost and improve power grid resilience, suitable for complex scenarios during peak.

[0046] Based on the reconstructed topology, inject chaotic anti-phase signals to offset non-periodic harmonics, effectively block multi-source interference paths, reduce peak efficiency loss and transformer overheating risk, reduce cable insulation breakdown rate, improve overall power grid stability, compensate for harmonic superposition and resonance caused by nonlinear load, and respond to "harmonic storm".

[0047] Real-time learning of neural network innovatively breaks through the delay of traditional scheduling, reduces operating cost and improves immunity to variant harmonics. The innovative mechanism of optical topology scheduling strategy adapts to multiple scenarios, reduces device overheating and misoperation, and improves overall scheduling efficiency. The innovative consensus mechanism of harmonic entropy weight proof reduces maintenance cost, enhances system resilience and fairness. BRIEF DESCRIPTION OF DRAWINGS

[0048] Figure 1 A step schematic diagram of a new energy efficient scheduling method based on a smart grid according to the present application;

[0049] Figure 2 A structure schematic diagram of a new energy efficient scheduling system based on a smart grid according to the present application. DETAILED DESCRIPTION

[0050] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0051] Embodiment 1, please refer to Figure 1 and Figure 2 The new energy efficient scheduling method based on a smart grid according to the present application comprises:

[0052] During the peak demand period in the city, the harmonic pollution is caused by the multi-source interaction of solar or wind inverter and household appliances (such as electric vehicle chargers, variable frequency air conditioners and LED lighting systems). This pollution is not the traditional low-frequency harmonic (usually in the range of 50-2500Hz, caused by industrial loads), but is concentrated in the medium-high frequency band of 2-9kHz, and its generation mechanism is highly dependent on the dynamic coupling of the nonlinear characteristics of distributed new energy equipment and the intensive consumption load in the city, making it difficult for the existing scheduling system to effectively suppress it through standard filters (such as passive LC filters).

[0053] Solar panels or wind turbines convert DC power into AC power through inverters and connect to the smart grid. These inverters use pulse width modulation (PWM) technology, and when the load switches frequently (such as the output of solar energy decaying during the evening peak period, and the inverter frequently adjusting the power factor), high-frequency switching noise will be generated. This noise appears as a sharp harmonic in the 2-9kHz range, especially in urban environments where multiple inverters are deployed synchronously, and the harmonic signal is amplified when multiple inverters operate synchronously, forming a resonance effect.

[0054] During the peak demand period, electric vehicle chargers (especially in fast charging mode, using switching power supplies) will introduce nonlinear loads, generating similar high-frequency harmonics. At the same time, other household appliances such as variable frequency air conditioners (compressor variable speed operation) and smart home devices (power electronic converters) will further inject harmonics. These loads share the same low-voltage distribution network branch with solar or wind inverter, causing harmonics to enhance each other through coupling paths (such as shared transformers or cables), forming multi-source interference. For example, a typical electric vehicle charging station may inject 3-5kHz harmonic current during charging, while a nearby solar inverter will respond by amplifying it to the 7-9kHz range, causing the overall total harmonic distortion (THD) rate to exceed the standard limit (IEEE519 standard requires THD <5%, but the actual value can reach 15-20%).

[0055] At the same time, due to the surge in demand during the peak demand period, the load imbalance of the power grid (such as the concentration of air conditioning loads in some neighborhoods) will cause the switching frequency of the inverter and the load to be mismatched, further exciting harmonic resonance. Unlike conventional, this harmonic is not static, but varies with real-time power fluctuations, for example, temporary cloud cover of solar energy causes inverter output transient, triggering harmonic peaks to rise 30-50% from baseline level within seconds.

[0056] The harmonic problem is most serious when the evening peak demand of residential electricity coincides with the decay of solar energy, because at this time the inverter is in low power mode (output <50% rated), which is more prone to generate high-frequency noise, while the demand for electric vehicle charging is also at its peak (for example, 50 EVs in a small community charging simultaneously), forming a "harmonic storm".

[0057] Therefore, harmonic pollution has an impact on grid efficiency and stability, such as causing power factor to drop, increasing reactive power consumption, especially during peak hours, which means that the available power is greatly wasted due to harmonic loss, for example, the effective output of a 100kW solar array may be reduced by 10-20kW due to harmonics, forcing the dispatch system to compensate from the external grid or storage, increasing operating costs.

[0058] Harmonic pollution also causes an increase in equipment failure rate, harmonic-induced transformer core hysteresis loss and eddy current loss intensify, causing overheating, especially during peak hours of urban distribution transformer load use, overheating can accelerate insulation aging, failure rate rises, there are also harmonic amplification current leading to capacitor bank resonance explosion due to overvoltage, high-frequency interference disturbing control signals causing relay misoperation (for example, protection relays may trigger circuit breakers incorrectly, causing unnecessary local power outage), etc. If not handled, harmonics can also interact with emerging loads (such as 5G base station power supplies), further pushing the frequency to higher segments, causing long-term grid degradation, such as increased cable insulation breakdown rate, leading to skyrocketing maintenance costs.

[0059] Embedding diamond NV color center micro sensors in the DC or AC port of solar or wind power inverters can be connected through a soldered interface, powered by new energy. Form a networked array; networked array supports distributed deployment, such as 2x2 or 3x3 grid, suitable for large-scale application of urban power grid (such as multiple inverter nodes in a small area), nodes form star or mesh topology, short response time, suitable for real-time harmonic monitoring, each node is equipped with 4-6 sensors; to achieve redundancy and multi-angle measurement, ensuring that the key interfaces of the inverter are covered (DC side to capture DC noise, AC side to capture AC harmonics), and the signal robustness is enhanced through the array, covering the entire power consumption area or small area range.

[0060] When the sensor is working in real time, first use continuous laser (wavelength about 532nm) to pump NV center, make it enter excited state; then apply microwave pulse (frequency about 2.87GHz, match NV spin resonance) to detect local magnetic field or electric field change, and convert the detected change into fluorescence signal, then convert it into digital signal through photodetector, sampling rate greater than 100kHz to ensure capturing fine-grained changes of high-frequency harmonics (such as cloud cover causing inverter output fluctuation, transient peak rising 30-50% within seconds), data is stored in time series form (for example, generating 10MB data packet per second), and time stamp and networked array node position are labeled;

[0061] The reason for using continuous laser pumping to bring the NV center into the excited state is that the working basis of the NV color center sensor is the "nitrogen-vacancy" defect in the diamond crystal. These defects are stable quantum bits at room temperature, but to make them active and sensitive to external electromagnetic fields, they must first be lifted from the ground state (low energy state) to the excited state (high energy state). Continuous laser pumping is this activation process, which provides energy to allow the electron spin of the NV center to enter a measurable quantum state in response to weak harmonic signals (such as 2-9 kHz electromagnetic disturbances).

[0062] After pumping, the electron spin of the NV center is in the excited state, but to accurately detect external changes (such as harmonic-induced magnetic or electric field fluctuations), the spins need to be "tuned". Microwave pulses (frequency about 2.87 GHz) interact with the spins of the NV center through spin resonance: if there is a local magnetic or electric field disturbance (such as inverter PWM noise or electric vehicle charging harmonics), it will change the energy level of the spin, and the microwave pulse will amplify this change, converting these disturbances into quantifiable quantum signals. Without this step, the sensor cannot distinguish between noise and useful signals.

[0063] External magnetic or electric field disturbances will change the electron spin flip probability of the NV center, modulating the intensity and pattern of fluorescence, for example, strong harmonic disturbances will cause the fluorescence intensity to decrease (because more electrons enter the dark state). When the electrons return to the ground state, they release photons, which are captured by the collection mirror and filter, forming a variable-intensity fluorescence stream, which forms the fluorescence signal that directly encodes harmonic information (such as amplitude and phase), with high sensitivity and the ability to capture hidden non-periodic variations (such as "harmonic storm" transients).

[0064] The fluorescence signal is analog (continuous light intensity variation), but subsequent processing requires computer algorithms. Converting to a digital signal enables quantization, storage, transmission, and analysis, making it easy to integrate into digital systems of the smart grid (such as edge processors or cloud platforms). In addition, the digital form facilitates the labeling of timestamps and node locations, supporting real-time networked monitoring.

[0065] The fluorescence signal is first captured by a photodetector (such as a photomultiplier tube or a silicon photodiode), converted into an analog voltage (high fluorescence intensity → high voltage), and then input into a high sampling rate ADC (>100kHz) to sample the voltage as discrete digital values (such as 16-bit resolution, each sample representing a time point). The sampling rate is set to 120-200kHz to cover the Nyquist frequency of the 2-9kHz harmonic (the sampling rate must be at least 2 times the highest frequency). The data is stored in time series format (such as CSV or binary files) and automatically labeled with timestamps (such as UTC format) and node locations (such as GPS coordinates or grid IDs).

[0066] The output raw data includes fluorescence intensity time series, phase offset vector, amplitude spectrum, auxiliary environmental variables (such as local temperature or inverter power), and labeled timestamp and node location information. It achieves fine-grained capture of hidden harmonics with an accuracy of more than 95%, far exceeding the 1kHz sampling limit of traditional sensors.

[0067] Among them, the fluorescence intensity time series: represents the change of fluorescence brightness with time, such as, [time t1: intensity 0.85, t2: intensity 0.92], encoding harmonic amplitude and phase.

[0068] Phase offset vector: a vector extracted from the fluorescence pattern, such as, [offset angle: 0.001°, frequency: 4kHz]), representing the timing disturbance of the harmonic.

[0069] Amplitude spectrum: spectral representation of harmonic intensity, such as, peak list in 2-9kHz range, [3kHz: amplitude 0.7, 7kHz: amplitude 1.2]).

[0070] Auxiliary environmental variables: local context data, including temperature (25°C), inverter power (50% load), load (such as electric vehicle charging) input current (10A), used to correct noise.

[0071] Timestamp: such as, 2023-10-01 18:00:00.123.

[0072] Node location: such as, latitude 39.9°, longitude 116.3° or grid ID: Node-5.

[0073] Temperature rise will amplify the electromagnetic disturbance caused by inverter power, while high load current may combine with power fluctuations to produce additional pulse noise, causing further distortion of the fluorescence signal. By integrating these variables, a unified correction strategy is generated to ensure that the corrected data more accurately reflects the hidden variations and multi-source coupling (such as inverter noise and electric vehicle charging interaction) of unconventional harmonics in the 2-9kHz high frequency band. Suitable for variable urban environments during peak hours.

[0074] First, extract all relevant auxiliary variables from the raw data, including temperature readings, inverter power levels, and load input current values, which are collected in real time by sensors, such as temperature updated every second, inverter power and load current updated every millisecond. And use the timestamp to align these variables with the fluorescence intensity time series; ensure that each time point has a complete corresponding data set, forming a unified time series data set, a data set at a time point contains fluorescence intensity value, current temperature, power percentage and current intensity.

[0075] After that, check if the temperature exceeds the temperature threshold (e.g., set to 30°C), if yes, increase the correction weight of inverter power and load current, e.g., increase the weight by 20%, because high temperature will amplify electromagnetic disturbance; if not, keep the default weight; after that, check if the load input current is higher than the average and the inverter power exceeds the expectation (e.g., set to 50%), if yes, mark as high interaction scenario, increase the joint weight of current and power (e.g., by 30%), prioritize the current-power coupling. Otherwise, keep the weight after the last step; then generate a weight vector (e.g., temperature weight 1.0, power weight 1.2, current weight 1.1) for subsequent correction.

[0076] Start the correction sequence based on the weight vector, first, calculate the base bias using temperature, specifically, if the temperature is higher than the reference value 25°C, slightly amplify the fluorescence intensity to compensate for the thermal-induced decay; if the temperature is lower than the reference value, slightly decrease, if the temperature exceeds the temperature threshold, additionally increase the fluorescence intensity based on the reference value adjustment, output the preliminary corrected fluorescence intensity time series.

[0077] Wherein, the above-mentioned slight adjustment of the fluorescence intensity sequence is usually controlled within the range of 1-5% (based on empirical test, to avoid excessive adjustment leading to signal distortion), this range is determined by simulating the city peak period scenario (e.g., evening EV charging + high temperature), for example, in laboratory tests, 1-3% adjustment can compensate for 80% of thermal decay, and 5% is the upper limit to prevent introducing noise. The adjustment is applied point by point, the average change of the overall sequence does not exceed 3% to maintain the natural fluctuation of the signal.

[0078] Specifically, slight amplification refers to increasing the fluorescence intensity value by 1-3% to compensate for high temperature causing NV center quantum state to lose faster, resulting in underestimated intensity, if the temperature is extremely high (e.g., 40°C), the degree can reach 4%, but not more than 5% to avoid overcompensation.

[0079] Slight decrease refers to reducing the fluorescence intensity value by 1-2% to avoid the possibility of low temperature causing the signal to be too stable and exaggerating the peak value.

[0080] Slight increase refers to an additional intensity adjustment range of 0.5-1% based on the base temperature correction to reflect the amplification effect of high temperature on power disturbance, because inverter power noise is more easily disturbed by fluorescence at high temperature. This increase is only applied when the situation occurs, and does not exceed 1% to maintain balance.

[0081] Example: Assume a time point t the raw intensity of fluorescence I_raw(t) = 0.75, temperature = 35°C (> 25°C, trigger base amplification), power = 60% (medium-high), current = 10A (higher than average 5A). Satisfy temperature exceeds temperature threshold (because temperature > 30°C), increase weight. First slightly amplify to 0.765 (2% compensation for thermal decay), then slightly increase to 0.770 (total 2.7%) due to marker, output, improve the accurate capture of harmonic peaks.

[0082] Then, on the preliminary sequence, continue to process the inverter power. First, calculate the deviation according to the power level, if the inverter power is higher than the median value (the middle value of the maximum and minimum value), slightly amplify the fluorescence intensity to compensate for internal field disturbance; if the inverter power is lower than the median value, reduce it to remove noise amplification. Integrate interactive effects: if the temperature exceeds the temperature threshold, increase the amplification effect of power correction; if the load input current is higher than the average value, reduce the fluorescence intensity again on the basis of median and temperature adjustment to balance the current-power coupling. Output updated sequence;

[0083] Wherein, the above-mentioned slight amplification refers to increasing the fluorescence intensity value by 1-3%, and the reduction refers to reducing 1-2%, and the increase of the amplification effect of power correction refers to additionally increasing the amplification amplitude by 0.5-1% on the basis of the basic adjustment, to reflect the amplification of power disturbance by high temperature (high temperature aggravates electromagnetic field change). For example, if the basic adjustment has amplified the intensity from 0.80 to 0.816 (2%), and there is a high temperature marker, further adjust to 0.820-0.824 (total amplification 2.5-3%). Reduce the fluorescence intensity again on the basis of median and temperature adjustment is to additionally reduce the amplitude by 0.5-1% on the basis of the basic adjustment, to balance the current-power coupling (high current will interact with power to amplify deviation).

[0084] Finally, according to the current value, subtract the pulse offset, and scale the fluorescence intensity according to the proportion of current relative to the average value (if the current is higher than the average, slightly amplify to compensate for pulse amplification; if lower than the average, reduce to remove offset), if the high interaction scene is marked, increase the scaling effect of current correction; if the temperature exceeds the temperature threshold, reduce the offset subtraction to prevent overcorrection, output the fully integrated sequence, and update the original data to form the corrected data;

[0085] Where, according to the current value minus the pulse offset refers to subtract a small offset (absolute value 0.01-0.05, or relative 1-3%) from the intensity value, the scaling effect of increasing current correction refers to additionally increase the scaling amplitude by 1-2% on the basis of the basic scaling (amplification or reduction), and the offset subtraction refers to reduce the offset subtraction (the part of subtracting the pulse offset) by 0.5-1% amplitude (or absolute 0.005-0.01) to prevent over-correction (the offset is easy to be exaggerated at high temperature, resulting in too low intensity).

[0086] Compare the indicators of the sequences before and after correction, for example, calculate the signal-to-noise ratio or variance change to verify whether the interference is reduced to less than 5%. If it is less than 10%, adjust the weight rule iteratively and reapply the correction.

[0087] By removing the composite interference of these variables, the signal-to-noise ratio and accuracy of the signal are improved, and the misjudgment of hidden harmonics is ultimately reduced, and reliable input is provided for subsequent prediction.

[0088] For the corrected data, select a measurement basis (such as Pauli basis or custom spin projection basis) to map the fluorescence intensity time series to the quantum Hilbert space (a multidimensional mathematical space with dimensions usually 4x4 or higher, representing the combination of spin and phonon states), form an initial rough matrix, extract projection values from the phase offset vector and amplitude spectrum (for example, take the offset angle as a sample and the amplitude peak as a weight to calculate the inner product projection), apply the maximum likelihood estimation algorithm to iteratively optimize the initial rough matrix, gradually adjust to match the extracted projection values, so that the matrix is positive definite (all eigenvalues in the matrix are non-negative, indicating physical validity) and the trace is 1 (total probability normalization). Finally, use the partial trace operation to remove irrelevant subspaces (such as external noise states, take the trace of the noise dimension to reduce the matrix and retain the core harmonic information). The entire reconstruction is completed on the node processor, supporting parallel computing (for example, block processing of time series data), outputting the final harmonic phase matrix (a numerical matrix, such as a 4x4 array, representing the phase and amplitude distribution of the 2-9kHz frequency band), and accompanying time stamp and node position;

[0089] Through the above processing, the matrix accurately captures the quantum correlation of multi-source coupling (such as the interaction of 3-5kHz harmonics injected by electric vehicles and 7-9kHz amplified by inverters), improves the phase resolution, which is much higher than traditional methods. Reduce errors caused by concealment problems.

[0090] Applying singular value decomposition (SVD) technique to the harmonic phase matrix, the harmonic phase matrix is decomposed into left singular vectors, singular values, and a diagonal matrix, where the singular values represent the feature strength (e.g., values > 0.5 represent dominant paths). Then, map to physical features: extract coupling paths from the left singular vectors (e.g., path ID corresponds to "electric vehicle charger → transformer → inverter"), combine with amplitude spectrum to calculate frequency patterns and phase offsets, auxiliary variables are used for weighting (e.g., reduce noise or weak path weight at high temperature). Finally, filter out secondary features (remove features with singular values < 0.5), generate a list of coupling path features (e.g., [path 1: frequency 3-5 kHz, strength 0.7, phase offset 0.002°; path 2: frequency 7-9 kHz, strength 1.2]), with timestamps and node locations; accurately locate multi-source interference (e.g., resonance triggered by air conditioning load concentration), greatly increase the accuracy of feature extraction, provide quantitative basis for prediction, and reduce analysis blind area.

[0091] Assume the reconstructed harmonic phase matrix ρ is a 4x4 symmetric positive definite matrix (simplified representation, element values have been normalized, representing phase or amplitude distribution):

[0092] ρ=[0.25 0.10 0.05 0.02 0.10 0.30 0.15 0.08 0.05 0.15 0.20 0.12 0.02 0.08 0.12 0.25]

[0096] The scenario captured by this matrix is that rows or columns 1-2 correspond to low-frequency coupling (3-5 kHz, EV charging injection), rows or columns 3-4 correspond to high-frequency amplification (7-9 kHz, inverter response), timestamp is 2023-10-01 18:00:05, node location is grid ID Node-7 (near the cell charging station).

[0097] Applying singular value decomposition (SVD) technique, the left singular vector U (4x4 matrix, each column is a vector, representing the "direction" of the phase pattern, e.g., the first column [0.45, 0.50, 0.35, 0.20] represents a dominant direction, corresponding to low-frequency phase offset) is obtained:

[0098] U=[0.45 0.30 0.15 0.10 0.50 0.40 0.20 0.15 0.35 0.25 0.50 0.30 0.20 0.15 0.30 0.45]

[0102] Sigma Σ (diagonal matrix, 4x4, only diagonal has values, representing eigen-strength, from large to small: 1.2, 0.7, 0.4, 0.2, e.g., 1.2 represents the strongest eigen, 0.4 represents a weaker eigen.

[0103] Σ = [1.2 0 0 0 0 0.7 0 0 0 0 0.4 0 0 0 0 0.2]

[0107] Right and left singular vectors VT (4x4 matrix, transposed form, representing the "direction" of the amplitude pattern):

[0108] VT = [0.40 0.35 0.25 0.20 0.45 0.50 0.30 0.15 0.20 0.25 0.45 0.40 0.15 0.10 0.20 0.35]

[0112] Eigenvalues > 0.5 (here 1.2 and 0.7) represent dominant paths, proceed to next step of mapping.

[0113] The U-vectors corresponding to the dominant eigenvalues (1.2 and 0.7) represent the path direction. For example, the first column U-vector [0.45, 0.50, 0.35, 0.20] (corresponding to eigenvalue 1.2) maps to path ID1: "EV charger → shared cable → inverter" (since the first two elements of the vector are high, indicating low-frequency coupling); the second column [0.30, 0.40, 0.25, 0.15] (eigenvalue 0.7) maps to path ID2: "transformer → inverter amplification" (the last two elements are higher, indicating high-frequency interaction). The weak vectors (eigenvalues 0.4 and 0.2) are not mapped for now, pending filtering.

[0114] Weight the peak values of the amplitude spectrum by the left singular vectors for each path. For example, for path ID1 (eigenvalue 1.2), the weighted average frequency = (3kHz 0.7 + 5kHz 0.4) ÷ (0.7 + 0.4) x (eigenvalue weight 1.2) ≈ 3.8kHz (low-frequency pattern, emphasizing load current injection); for path ID2 (eigenvalue 0.7), the average frequency = (7kHz 1.2 + 9kHz 0.3) ÷ (1.2 + 0.3) x 0.7 ≈ 7.6kHz (high-frequency pattern, emphasizing inverter amplification). The frequency patterns manifest as ranges: path1 = 3-5kHz, path2 = 7-9kHz.

[0115] Path ID1 U-vector first element ÷ second element ratio (0.45 ÷ 0.50 = 0.9), phase offset = atan2(0.9) ≈ 0.002° (small offset, indicates stable coupling); Path ID2 ratio (0.25 ÷ 0.15 = 1.67), offset = atan2(1.67) ≈ 0.005° (slightly larger offset, indicates variability).

[0116] Fusion temperature = 35°C (high, reduces weak path weight by 0.8x, e.g. singular value 0.7 down to 0.56); inverter power = 60% (medium-high, increases path ID2 weight by 1.1x to 0.616); load current = 10A (high, jointly weighted path ID1.2x to 1.44). Adjusted intensities: Path 1 = 0.8 (original 0.7 1.2 ÷ 0.9 temperature discount), Path 2 = 1.3 (original 1.2 1.1).

[0117] Output preliminary features (path ID, frequency pattern, phase offset, weighted intensity), e.g. Path 1: 3-5kHz, offset 0.002°, intensity 0.8; Path 2: 7-9kHz, offset 0.005°, intensity 1.3; weak paths temporarily retained (intensities 0.3-0.4).

[0118] Check weighted singular values: Path 1 = 1.44 (>0.5, retain); Path 2 = 1.3 (>0.5, retain); weak path = 0.32 (original 0.4 x 0.8 temperature discount, <0.5, filtered out); another weak path = 0.16 (<0.5, filtered). Filtering based on cutoff threshold 0.5 (empirical value, >0.5 indicates dominant >50% energy).

[0119] Compile retained features into structured list, taking intensities as weighted values, final list:

[0120] [Path 1: frequency 3-5kHz, intensity 0.8, phase offset 0.002°];

[0121] [Path 2: frequency 7-9kHz, intensity 1.3, phase offset 0.005°]; accompanied by timestamp (2023-10-01 18:00:05) and node location (grid ID Node-7).

[0122] Calculate Von Neumann entanglement entropy from harmonic phase matrix (calculate the logarithm and trace of harmonic phase matrix through preset formula to quantify entanglement degree); then, compare the entropy value with the entropy threshold (entropy threshold is initially 0.8, which can be dynamically adjusted), if the entanglement entropy value exceeds the entropy threshold, simulate storm evolution combined with coupling path characteristics, output an alarm package containing prediction time, storm probability, coupling path characteristics, harmonic phase matrix summary, and timestamp and node location, and transmit it to the next layer through an encrypted channel (such as 5G or a dedicated link), otherwise, no output, continue the loop.

[0123] Where, the preset formula Von Neumann entanglement entropy: S=-Tr(ρlogρ), where Tr is the trace (sum of diagonal elements of the matrix), log is the matrix logarithm, and ρ is the harmonic phase matrix; the storm evolution simulation combined with path characteristics can use a statistical model (such as exponential growth rate based on path intensity) to simulate evolution. For example, probability=1-exp(-k×intensity_sum), where k=0.5 (empirical constant) and intensity_sum=total path intensity. The prediction time is based on the phase offset.

[0124] The alarm package can be in the form of,

[0125] Prediction time: within the next 3-5 seconds (based on simulation, calculated from the current timestamp);

[0126] Storm probability: 80% (high risk, based on intensity and offset);

[0127] Path characteristics: [Path 1: frequency 3-5 kHz, intensity 0.8, phase offset 0.002°; Path 2: frequency 7-9 kHz, intensity 1.3, phase offset 0.005°] (complete list);

[0128] Phase matrix summary: {dimension: 4x4, entropy: 0.95, main elements: [0.25, 0.30, 0.20, 0.25] (diagonal simplification)} (summary avoids transmitting the full matrix, highlighting key values);

[0129] Timestamp: 2023-10-01 18:00:05.

[0130] Node location: Grid ID Node-7 (near the cell charging station);

[0131] Through the above, "harmonic storm" can be warned 1-5 seconds in advance, triggering downstream isolation and scheduling, greatly reducing efficiency loss, and greatly improving overall prediction accuracy.

[0132] A silicon-based photonic crystal assembly with dimensions of 10 cm x 10 cm is manufactured using 3D printing technology, integrated with a liquid crystal driving circuit, and subjected to preliminary electrical control testing. The silicon-based photonic crystal assembly that passes the test is installed (fixed on the surface of a transformer or cable through attachment) on a critical coupling path (such as the line from an electric vehicle charging station to an inverter), connected to the quantum harmonic pre-diagnosis layer through an electrical control interface, receives an alarm package, sets the voltage applied by the electrical control liquid crystal metasurface based on path characteristics (such as frequencies of 3-5 kHz or 7-9 kHz) and storm probability (if the probability is higher than the safety threshold), and reconstructs the topological structure; then, based on load adaptive adjustment parameters (such as increasing the amplitude of chaotic signals when the load input current is higher than the average value), a chaotic anti-phase signal is generated and injected to cancel the non-periodic harmonics; the synchronization stability is verified, and if it is stable, it is applied to the power grid, otherwise, it returns to reconstruction and adjustment of the bandgap range; the reconstructed topological configuration and chaotic anti-phase signal summary are output, with a timestamp and node position;

[0133] wherein the topological configuration includes the reconstructed topological structure and bandgap parameters, such as "ring topology, bandgap 3-5 kHz width 2 kHz, isolation 40 decibels", and the signal summary includes generation parameters and cancellation efficiency, such as "anti-phase chaotic wave, amplitude -0.5V, for 7-9 kHz, cancellation efficiency 85%".

[0134] The preliminary electrical control test is to verify the electrical control performance, stability, and response capability to harmonic frequencies of the manufactured silicon-based photonic crystal assembly (including waveguide arrays and liquid crystal metasurfaces), to ensure that the assembly can work normally before actual deployment, without problems such as short circuit, response delay, or isolation failure. This test is carried out in a laboratory environment and usually lasts for 1-2 days.

[0135] Based on path characteristics and storm probability, the method for setting the voltage applied by the electrical control liquid crystal metasurface to reconstruct the topological structure includes;

[0136] Extract the coupling path features and storm probability in the alarm package, set the initial topology as linear, which represents a linear band gap, if the coupling path frequency is less than 3 kHz and the storm probability is greater than the first limit value (such as set to 75%), adjust the voltage to 4V, adjust the topology to spiral, form a spiral band gap, if the coupling path frequency shows 3-5 kHz and the storm probability is greater than the second limit value (such as set to 80%), adjust the voltage to 5V, adjust the topology to ring, form a ring band gap; if the coupling path frequency shows 5-7 kHz and the storm probability is greater than the third limit value, adjust the voltage to 6V, adjust the topology to grid, form a grid band gap, if the coupling path frequency shows 7-9 kHz and the storm probability is greater than the fourth limit value, adjust the voltage to 7V, adjust the topology to star, form a star band gap; if the coupling path shows resonance energy dispersion requirements and the storm probability is greater than the fifth limit value, adjust the voltage to 8V, adjust the topology to fractal, form a fractal band gap; if the coupling path shows multiple path superposition and the storm probability is less than or equal to the second limit value, keep the linear topology and fine-tune the voltage to 3V.

[0137] Wherein, the coupling path shows resonance energy dispersion requirements means that the path feature list in the alarm package shows that the phase offset of multiple paths is large (such as > 0.004°) or the intensity distribution is relatively uniform (such as the intensity difference of multiple paths < 0.5), indicating that harmonic energy "resonates" between frequency points and needs to be dispersed. Multiple path superposition means that the path feature list in the alarm package shows that multiple paths (such as more than 2 paths) have similar intensity (such as a difference of less than 0.3) and overlapping or adjacent frequency ranges, indicating that harmonics from multiple sources (such as EV + air conditioner + inverter) "superimpose" to form a composite interference.

[0138] By applying voltage to the electrically controlled liquid crystal super surface, the refractive index distribution of the photonic crystal waveguide array is changed, a new topology is formed, and the topology structure directly determines the band gap range (frequency "void" that does not allow harmonics to pass). Reconstructing the topology essentially dynamically defines the band gap, making the isolation targeted, for example, a ring topology creates a 3-5 kHz band gap (width 2 kHz, blocks load current injection); a star topology extends to a 7-9 kHz band gap (width 2 kHz, blocks inverter amplification).

[0139] Based on Chua circuit to generate chaotic anti-phase signal, the purpose is to create a signal "mirror opposite" to the input harmonic, injected into the power grid to neutralize the aperiodic variation (such as the random peak value of "harmonic storm"), for example, for aperiodic 3 kHz, the original peak value +1V, the inverse -1V, the sum ≈0V, verify that more than 70% of the aperiodic harmonics are canceled, reduce the storm risk, stability greater than 95%, verified, these are edge computing, through the output interface of the component to directly inject the anti-phase signal into the power grid line.

[0140] By constructing topological boundary states in the 2-9 kHz frequency range, harmonic waves are made to propagate unidirectionally or be absorbed, and the dynamic reconfiguration of the bandgap is used to block coupling paths and counteract non-periodic harmonics.

[0141] The integrated neuromorphic computing chip uses historical data to pre-train a spiking neural network model to simulate load fluctuations. The pre-trained neuromorphic computing chip is embedded in the dispatch center, connected to the inverter and storage center, forming a distributed control network, connected to the optical topology reconfiguration layer through the interface, receiving the reconfigured topology configuration and injection signal summary, based on the topology configuration (such as ring-shaped bandgap or star-shaped isolation zone) and storm probability (if the probability is higher than the limit value), running the spiking neural network model, and dynamically responding to specific scenarios based on the optical topology scheduling strategy to generate scheduling instructions; then, the space-time memory model is used to further optimize the instructions and verify the stability of the instructions, if the stability (the stability of the instructions is verified by checking the response delay less than 3 milliseconds), then applied to the power grid, otherwise, return to the generation step to update the model parameters; output the optimized scheduling instructions and performance summary, with timestamp and node location;

[0142] The scheduling instructions are actual operation commands generated by the spiking neural network model and the space-time memory model, used to dynamically adjust the new energy components (such as inverters, storage systems or load distribution) in the smart grid to respond to topology configuration and storm prediction, achieving efficient scheduling.

[0143] Including adjustment parameters, target components and execution conditions, represented in a structured format (such as JSON or command string), example (assuming input "star-shaped isolation zone, probability 85%", corresponding to electric vehicle group charging peak scenario):

[0144] Instruction format: {type: block priority, adjustment: reduce inverter output by 15%, target: Node-7 inverter, compensation: prioritize wind energy storage allocation 20kWh, condition: storm probability > 80%, expected effect: total harmonic distortion rate down 15%}; performance summary representation: instruction generation delay 2.5ms, prediction accuracy 96%, expected effect: harmonic down 15%, load balancing 92%, verified stable.

[0145] The spiking neural network simulates brain neurons, learns harmonic propagation patterns in real time by inputting topology configuration and storm probability (such as the network "remembering" the scenario of ring-shaped bandgap corresponding to cloud cover transient in historical data). Then, the network generates a targeted strategy (such as "isolation priority strategy", instruction to reduce inverter output by 10%) based on the optical topology scheduling strategy. The space-time memory model further optimizes this strategy.

[0146] Example: input "ring-shaped bandgap + probability 82%", SNN learns to generate strategy: "isolation priority, reduce peak rise by 30-50%".

[0147] The method for dynamically responding to specific scenarios by the light topology scheduling strategy comprises: extracting topology configuration and storm probability, setting an initial scheduling strategy as a default linear allocation, which is manifested as uniform power output, if the topology configuration shows a ring-shaped forbidden band and the storm probability is greater than a first probability threshold (a scenario such as cloud layer shielding transient), adjusting to an isolation priority strategy to reduce inverter output (such as reducing 10%) to isolate the harmonic amplification path; if the topology configuration shows a star-shaped forbidden band and the storm probability is greater than a second probability threshold (a scenario such as electric vehicle group charging peak), adjusting to a blockage priority strategy to preferentially use new energy compensation (such as wind energy compensation) to block the superposition of multi-car harmonics; if the topology configuration shows a fractal forbidden band and the storm probability is greater than a third probability threshold (a scenario such as air conditioner group starting resonance), adjusting to a dispersion priority strategy to disperse resonant energy to achieve load balancing greater than 90%; if the topology configuration shows a grid forbidden band and the storm probability is less than or equal to the first probability threshold, maintaining the default linear allocation and fine-tuning the instructions to optimize storage allocation.

[0148] The dynamic response of the light topology scheduling strategy to specific scenarios is achieved by simulating brain neurons in a neuromorphic chip to learn harmonic patterns in real time, so as to respond to scenarios such as cloud layer shielding transient, electric vehicle group charging peak, or air conditioner group starting resonance.

[0149] The isolation priority strategy refers to preferentially isolating the harmonic amplification path, so that the peak value is reduced by 30-50%. The blockage priority strategy refers to preferentially blocking the superposition of multi-car, so that the total harmonic distortion rate is reduced by 15%. The dispersion priority strategy refers to dispersing resonant energy, so that the load balancing is greater than 90%.

[0150] By simulating brain neurons in a neuromorphic chip, learning harmonic propagation patterns, and predicting load fluctuations in a space-time memory model, an immune strategy is generated to achieve a scheduling instruction generation delay of less than 3 milliseconds.

[0151] The scheduling instructions are encrypted by the scheduling center and distributed to new energy nodes, the new energy nodes submit harmonic suppression proofs to the blockchain, the proofs are verified based on harmonic entropy weight, the blockchain releases compensation quotas to demand-side load nodes, and the smart contract is automatically executed.

[0152] The scheduling center runs on a cloud server or a dedicated computer, which refers to the core control unit (also known as the central dispatch system or coordinator) of the smart grid, responsible for integrating upstream outputs, generating and distributing encrypted instructions to network nodes.

[0153] New energy nodes are distributed devices that receive and decrypt scheduling instructions and then execute suppression, such as photovoltaic (solar) power generation nodes, wind turbine nodes, and tidal energy or hybrid. Harmonic suppression proof, such as {node ID: Node-7, suppression effect: THD reduction 33%, entropy contribution: 0.85, timestamp: 2023-10-01 18:00:20}.

[0154] The consensus mechanism for harmonic suppression proof uses PoHE verification, calculates priority based on entropy, and allows nodes to obtain account rights by contributing harmonic suppression effects, for example: if the contribution is high, the node obtains account rights (prioritizes transaction processing); verification success rate > 99%, delay < 1s.

[0155] Demand-side load nodes refer to demand-side load devices such as electric vehicle charging stations, air conditioners, smart home loads, or industrial devices near wind farms, which release compensation amounts through smart contracts, such as energy points or economic incentives, to reward users who participate in suppression, such as EV users reducing charging to reduce harmonics, releasing amounts to charging piles (such as "10kWh free energy" or "0.5 yuan discount"), and the contract code checks "suppression effect > 30%" to execute.

[0156] Embodiment 2, please refer to Figure 1 and Figure 2 , the unexplained part of this embodiment is described in embodiment 1, and a new energy efficient scheduling method based on smart grid is provided, including:

[0157] S1: Embed diamond NV color center micro sensors in the DC or AC port of the inverter in the new energy power grid to form a networked array, real-time collect and output raw data;

[0158] S2: Extract auxiliary environmental variables, correct based on temperature, inverter power and load input current, generate weight vector, adjust fluorescence intensity sequence layer by layer, form corrected data;

[0159] S3: Select measurement basis mapping to quantum Hilbert space, form initial rough matrix, extract projection value iteration optimization, output harmonic phase matrix;

[0160] S4: Decompose the harmonic phase matrix, extract the coupling path feature, calculate the entanglement entropy, if it exceeds the entropy threshold, simulate storm evolution, output alarm package, otherwise continue the loop;

[0161] S5: Reconfigure the topology based on the alarm package, inject chaotic anti-phase signals, determine whether to apply to the new energy power grid through stability verification, and output the configuration summary;

[0162] S6: Run the pulse neural network receiving the configuration summary, generate scheduling instructions based on the optical topology scheduling strategy, verify and output the scheduling instructions and performance summary;

[0163] S7: distribute the dispatching instruction to the new energy node, then submit the harmonic suppression proof for consensus verification, release the compensation quota to complete the execution.

[0164] A new energy efficient dispatching system based on smart grid, comprising:

[0165] Quantum sensing module: embeds a diamond NV color center micro sensor to collect raw data, corrects fluorescence intensity, and reconstructs a harmonic phase matrix to output an alarm package;

[0166] Optical topology reconstruction module: receives the alarm package to reconstruct the topology structure, injects a chaotic anti-phase signal, and outputs a configuration digest;

[0167] Dynamic scheduling module: generates a dispatching instruction based on the configuration digest, optimally allocates in response to a specific scenario, and outputs the dispatching instruction;

[0168] Blockchain consensus module: encrypts and distributes the instruction, verifies the harmonic suppression proof, executes compensation, and forms a closed loop.

[0169] Embodiment 3 discloses an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to realize the operation mode of the above-mentioned new energy efficient dispatching method based on smart grid.

[0170] Since the electronic device introduced in the embodiment is used to implement the new energy efficient dispatching method based on smart grid in the embodiment, the specific implementation of the electronic device and its various changes can be understood by those skilled in the art based on the new energy efficient dispatching method based on smart grid introduced in the embodiment, so the implementation of the electronic device in the method of the embodiment will not be introduced in detail. As long as the electronic device used to implement the new energy efficient dispatching method based on smart grid in the embodiment is implemented by those skilled in the art, it belongs to the scope of protection of the present application.

[0171] The above formulas are dimensionless values, and the formulas are obtained by software simulation of a large amount of data to obtain a formula of the most recent real situation. The preset parameters and threshold values in the formula are set by those skilled in the art according to the actual situation.

[0172] The above merely describes the preferred embodiments of the present application, and the protection scope of the present application is not limited to the above-described embodiments. Any technical solution falling within the concept of the present application shall fall within the protection scope of the present application. It should be noted that, for ordinary technical users in the technical field, several improvements and refinements without departing from the principle of the present application shall also be considered as falling within the protection scope of the present application.

Claims

1. A method for efficient dispatching of new energy sources based on smart grids, characterized in that, include: S1: In the new energy power grid, diamond NV color center micro sensors are embedded in the DC or AC ports of the inverter to form a networked array, which collects and outputs raw data in real time. S2: Extract auxiliary environmental variables, correct them based on temperature, inverter power and load input current, generate weight vectors, adjust fluorescence intensity sequence layer by layer to form corrected data; S3: Select the measurement basis and map it to the quantum Hilbert space to form an initial coarse matrix. Extract the projection values ​​and iteratively optimize to output the harmonic phase matrix. For the corrected data, a measurement basis is selected, and the fluorescence intensity time series is mapped to the quantum Hilbert space to form an initial coarse matrix. The projection values ​​are extracted from the phase offset vector and amplitude spectrum. The initial coarse matrix is ​​iteratively optimized to match the extracted projection values, so that the matrix is ​​positive definite and the trace is 1. The irrelevant subspace is removed using partial trace operations, and the final harmonic phase matrix is ​​output. S4: Decompose the harmonic phase matrix, extract coupling path features, calculate entanglement entropy, and if it exceeds the entropy threshold, simulate storm evolution and output an alarm packet; otherwise, continue the loop. The harmonic phase matrix is ​​decomposed into a left singular vector, singular values, and a diagonal matrix, where the singular values ​​represent the feature intensity. The coupling path is extracted from the left singular vector, and the frequency mode and phase shift are calculated by combining the amplitude spectrum. Auxiliary environmental variables are used for weighting, minor features are filtered, and a list of coupling path features is generated. The entanglement entropy is calculated based on the harmonic phase matrix. The entropy value of the entanglement entropy is compared with the entropy threshold. If the entanglement entropy value exceeds the entropy threshold, the storm evolution is simulated in combination with the coupling path characteristics, and an alarm packet is output, which includes the prediction time, storm probability, coupling path characteristics, harmonic phase matrix summary, timestamp and node position. Otherwise, there is no output, and the loop continues. S5: Reconstruct the topology based on the alarm packet, inject chaotic inverse signals, determine whether to apply it to the new energy grid through stability verification, and output a configuration summary; A silicon-based photonic crystal assembly is installed in the critical coupling path and connected to the quantum harmonic prediagnostic layer via an electronically controlled interface. It receives alarm packets, sets the voltage applied to the electronically controlled liquid crystal metasurface based on coupling path characteristics and storm probability, and reconstructs the topology. Based on load-adaptive parameter adjustments, a chaotic anti-phase signal is generated and injected. Synchronization stability is verified; if stable, it is applied to the power grid; otherwise, reconstruction is returned and the bandgap is adjusted. The reconstructed topology configuration and a summary of the chaotic anti-phase signal are output, along with timestamps and node locations. The topology configuration includes the reconstructed topology and bandgap parameters, and the chaotic inverse signal summary includes the generation parameters and cancellation efficiency. S6: Receive the configuration summary, run the spiking neural network, generate scheduling instructions based on the optical topology scheduling strategy, verify and output the scheduling instructions and performance summary; S7: The encrypted scheduling instructions are distributed to the new energy nodes. Subsequently, the harmonic suppression proof is submitted for consensus verification, and the compensation amount is released to complete the execution.

2. The method for efficient dispatching of new energy sources based on a smart grid according to claim 1, characterized in that, The method for real-time acquisition and output of raw data includes: Diamond NV color center micro-sensors are embedded in the DC or AC ports of the inverter to form a networked array, with each node equipped with the sensor. When the sensor is working in real time, it first uses a continuous laser to pump the NV center to enter the excited state; then it applies a microwave pulse to detect changes in the local magnetic field or electric field and converts the detected changes into a fluorescence signal, which is then converted into a digital signal by a photodetector with a sampling rate greater than 100kHz; the digital signal is stored in time series form. Output the raw data, which includes fluorescence intensity time series, phase shift vector, amplitude spectrum, auxiliary environmental variables, timestamps and node positions.

3. The method for efficient dispatching of new energy sources based on a smart grid according to claim 2, characterized in that, S2 includes: All relevant auxiliary environmental variables, including temperature, inverter power, and load input current, were extracted from the raw data and aligned with the fluorescence intensity time series using timestamps. Next, it checks whether the temperature exceeds the temperature threshold. If so, it increases the correction weights of inverter power and load current. If not, it keeps the default weights. Then, it checks whether the load input current is higher than the average value and whether the inverter power exceeds the expectation. If so, it is marked as a high-interaction scenario, and the joint weights of current and power are increased. Otherwise, it keeps the weights processed in the previous step and then generates a weight vector. The sequence is corrected based on the weight vector. First, the basic deviation is calculated using temperature. Specifically, if the temperature is higher than the reference value, the fluorescence intensity is increased; if the temperature is lower than the reference value, it is decreased; if the temperature exceeds the temperature threshold, the fluorescence intensity is increased in addition to the adjustment based on the reference value, and the fluorescence intensity time series after preliminary correction is output. Then, the deviation is calculated based on the power level. If the inverter power is higher than the median, the fluorescence intensity is amplified; if the inverter power is lower than the median, it is reduced; if the temperature exceeds the temperature threshold, the amplification effect of power correction is increased; if the load input current is higher than the average value, the fluorescence intensity is further reduced based on the median and temperature adjustment, and the updated sequence is output. Finally, the pulse offset is subtracted from the current value, and the fluorescence intensity is scaled according to the ratio of the current to the average value. If a high-interaction scene is marked, the scaling effect of the current correction is increased; if the temperature exceeds the temperature threshold, the offset subtraction is reduced. The fully integrated sequence is output, and the original data is updated to form the corrected data.

4. The method for efficient dispatching of new energy sources based on a smart grid according to claim 3, characterized in that, The method for reconstructing the topology by setting the voltage applied to the electro-controlled liquid crystal metasurface based on coupling path characteristics and storm probability includes: Extract the coupling path characteristics and storm probability from the alarm packet. Set the initial topology to linear, resulting in a linear bandgap. If the coupling path frequency is less than 3kHz and the storm probability is greater than the first threshold, adjust the voltage to 4V and adjust the topology to a spiral shape, forming a spiral bandgap. If the coupling path frequency is 3-5kHz and the storm probability is greater than the second threshold, adjust the voltage to 5V and adjust the topology to a ring shape, forming a ring bandgap. If the coupling path frequency is 5-7kHz and the storm probability is greater than the third threshold, adjust the voltage to 6V and adjust the topology to a grid shape, forming a grid bandgap. If the coupling path frequency is 7-9kHz and the storm probability is greater than the fourth threshold, adjust the voltage to 7V and adjust the topology to a star shape, forming a star bandgap. If the coupling path shows resonant energy dispersion requirements and the storm probability is greater than the fifth threshold, adjust the voltage to 8V and adjust the topology to a fractal shape, forming a fractal bandgap. If the coupling path shows multi-path superposition and the storm probability is less than or equal to the second threshold, maintain the linear topology and fine-tune the voltage to 3V.

5. The method for efficient dispatching of new energy sources based on a smart grid according to claim 4, characterized in that, S6 includes: A pre-trained spiking neural network model is embedded in the dispatch center, connecting the inverter and storage center to form a distributed control network. It is connected to the optical topology reconstruction layer through an interface, runs the spiking neural network model based on topology configuration and storm probability, and dynamically responds to specific scenarios based on the optical topology dispatch strategy, generates dispatch instructions, verifies the stability of the dispatch instructions. If stable, it is applied to the power grid; otherwise, it returns an update. The optimized dispatch instructions and performance summary are output. The method for dynamically responding to specific scenarios using the optical topology scheduling strategy includes: extracting the topology configuration and storm probability; setting the initial optical topology scheduling strategy to default linear allocation, which manifests as uniform power output; if the topology configuration displays a ring bandgap and the storm probability is greater than a first probability threshold, then the optical topology scheduling strategy is adjusted to an isolation-priority strategy to reduce inverter output; if the topology configuration displays a star bandgap and the storm probability is greater than a second probability threshold, then it is adjusted to a blocking-priority strategy to prioritize backup renewable energy compensation; if the topology configuration displays a fractal bandgap and the storm probability is greater than a third probability threshold, then it is adjusted to a distributed-priority strategy to distribute resonant energy; if the topology configuration displays a grid bandgap and the storm probability is less than or equal to the first probability threshold, then the default linear allocation is maintained.

6. The method for efficient dispatching of new energy sources based on a smart grid according to claim 5, characterized in that, The scheduling instructions are encrypted and distributed to the new energy nodes by the scheduling center. The new energy nodes submit harmonic suppression proofs to the blockchain, which are then verified. The blockchain releases compensation amounts to the demand-side load nodes, and the process is automatically executed based on smart contracts.

7. A smart grid-based high-efficiency dispatching system for new energy sources, used to implement the smart grid-based high-efficiency dispatching method for new energy sources as described in any one of claims 1-6, characterized in that, The smart grid-based high-efficiency dispatching system for new energy sources includes: Quantum sensing module: Embedded diamond NV color center micro-sensor to collect raw data, perform fluorescence intensity correction and harmonic phase matrix reconstruction, and output alarm packet; Optical topology reconstruction module: Receives alarm packets, reconstructs the topology structure, injects chaotic inverse signals, and outputs a configuration summary; Dynamic scheduling module: Generates scheduling instructions based on configuration summary, optimizes allocation in response to specific scenarios, and outputs scheduling instructions; Blockchain consensus module: encrypts and distributes instructions, verifies harmonic suppression proofs, executes compensation, and forms a closed loop.

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