Electric power carbon emission intelligent analysis system based on double carbon targets

Abstract

The invention relates to the technical field of data processing, in particular to an electric power carbon emission intelligent analysis system based on a dual-carbon target, which comprises a quantum security acquisition engine, a dynamic privacy calculation fusion device, a quantum and classical mixed twin modeling module, a multi-agent quantum decision engine and a cross-modal causal auditing platform. The dynamic privacy calculation fusion device aggregates the distributed carbon intensity feature vectors in a ciphertext state, and dynamically adjusts the Laplace noise amplitude based on a polarization key fragmentation strategy; the quantum decision engine optimizes and maps the unit commitment to an Isin model to output a Pareto solution set, and generates dynamic block chain contract terms; a cross-modal auditing platform monitors a carbon footprint boundary, and when a prediction deviation exceeds a threshold value, quantum branch parameters are calibrated through an anti-fact gradient and contract terms are reconstructed. According to the system, privacy security is maintained through frequency binding of quantum noise amplitude and channel bit error rate, prediction time lag caused by source-load coupling is solved through anti-fact gradient closed-loop calibration, and the low-carbon scheduling precision of the power system is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, and in particular to an intelligent analysis system for power carbon emissions based on a double carbon target. BACKGROUND

[0002] The power sector accounts for a significant proportion of greenhouse gas emissions, and its carbon emissions are mainly released in the form of carbon dioxide during the combustion of coal or gas in thermal power generation. In order to reduce the overall emission intensity of the power system, it is necessary to accelerate the large-scale application of renewable energy sources such as wind and photovoltaic power generation, and to use energy storage technology to improve power supply stability, supplemented by intelligent power grids to optimize load distribution and scheduling, gradually replacing the dominant position of fossil fuels, promoting clean energy structure transformation, and thus achieving long-term emission reduction and supporting the advancement of carbon neutralization.

[0003] The intelligent analysis of power carbon emissions based on the double carbon target faces the technical pain point of inadequate adaptability of carbon emission intensity prediction models. In the energy management system carbon cloud aggregation operation combined with market interaction scenarios, due to the randomness of distributed power output and meteorological factors, the calibration of carbon emission correlation models becomes more difficult, making it impossible for the platform to accurately capture the dynamic carbon trajectory evolution when virtual power plants participate in peak shaving services. For example, when aggregated new energy units respond to fluctuations in day-ahead market electricity prices, inaccurate carbon emission intensity prediction values will cause optimization algorithm bias, resulting in excessive aggregation scheduling of fossil fuel backup units, increasing the net carbon footprint and raising market transaction operation costs. SUMMARY

[0004] To address the deficiencies in the prior art, the present application provides an intelligent analysis system for power carbon emissions based on a double carbon target, which solves the problem of real-time correlation inaccuracy of carbon emission intensity prediction models under dynamic source-load coupling conditions.

[0005] To solve the above technical problems, the present application is as follows:

[0006] The intelligent analysis system for power carbon emissions based on a double carbon target provided by the present application comprises:

[0007] A quantum secure acquisition engine captures the vibration frequency spectrum of a wind turbine bearing in real time through a photonic crystal optical fiber sensor, simultaneously accesses an electricity spot market API to obtain millisecond-level electricity price time series signals, and analyzes microscale meteorological turbulence tensors issued by meteorological satellites. The vibration frequency spectrum of the wind turbine bearing is outputted as an intrinsic modal function through Hilbert-Huang transformation, a quantum true random number generator is driven to generate a polarization key sequence, and an encrypted data stream resistant to quantum attacks is constructed through a decoy state BB84 protocol.

[0008] The dynamic privacy computing fusioner receives the encrypted data stream, deploys a homomorphic encryption acceleration card to execute an improved federated learning feature alignment algorithm in a ciphertext state, aggregates carbon intensity feature vectors of distributed nodes, and controls the amplitude of Laplace noise injection according to a dynamic sharding strategy of the polarization key sequence;

[0009] The quantum and classical hybrid twin modeling module inputs the encrypted feature vector output by the dynamic privacy computing fusioner, constructs a bifurcated modeling topology, and the bifurcated modeling topology includes a classical branch and a quantum branch:

[0010] The classical branch models the continuous-time relationship between electricity price fluctuations and carbon trajectories by using a neural differential equation solver, and outputs a dynamic carbon intensity prediction tensor;

[0011] The quantum branch encodes the non-Gaussian volatility tail probability amplitude of wind and light power output by a quantum amplitude estimation algorithm, and outputs a volatility characteristic quantum state;

[0012] The multi-agent quantum decision engine receives the dynamic carbon intensity prediction tensor and the volatility characteristic quantum state, maps the unit commitment optimization problem to an Ising model, and generates a Pareto frontier solution set through quantum annealing optimization;

[0013] Based on the non-Gaussian volatility kurtosis value output by the quantum branch, a blockchain smart contract constraint clause is generated, which is dynamically reconstructed according to the real-time carbon prediction value;

[0014] The cross-modal causal audit platform monitors the carbon footprint boundary of the blockchain smart contract constraint clause, and when the prediction deviation exceeds the Smirnov threshold, locates the fault event causal chain through a differentiable causal graph, inputs calibration instructions to the rotation gate parameter space of the quantum branch through the counterfactual gradient backpropagation channel, and synchronously triggers the reconstruction operation of the smart contract constraint clause;

[0015] The sharding rule of the polarization key sequence is bound with the noise amplitude and the frequency, and for every 0.1% increase in quantum channel error rate, the noise amplitude is reduced by 5dB, so that the quantum branch does not trigger privacy leakage when capturing ±5σ volatility characteristics;

[0016] After the counterfactual gradient calibration instruction drives the rotation gate parameter update, it is fed back to the federated learning feature alignment algorithm to reweight the nodes, and a closed-loop calibration path is obtained, which is used to solve the time lag error of carbon trajectory prediction under dynamic source-load coupling conditions.

[0017] Further, the double carbon target-based power carbon emission intelligent analysis system of the present application, the quantum security acquisition engine constructs an anti-quantum attack encrypted data stream through the following steps:

[0018] The narrow-band photonic crystal fiber sensor deployed in the transmission chain bearing seat captures the fan bearing vibration spectrum data, and converts the Bragg grating reflection wavelength offset into vibration acceleration digital signals;

[0019] The laser Doppler velocimeter obtains the wind speed fluctuation component in real time, and correlates the wind speed fluctuation component with the time sequence turbulence intensity tensor according to the Euclidean distance rule;

[0020] The original meteorological data issued by the meteorological satellite interface is analyzed, and the micro-scale wind direction change probability matrix is extracted;

[0021] The fan bearing vibration spectrum data is processed by Hilbert-Huang transform, and the intrinsic mode function component including frequency and energy distribution is output;

[0022] The quantum true random number generator receives the intrinsic mode function component and generates a polarization encoding basis vector sequence;

[0023] The electro-optic modulator loads the polarization encoding basis vector sequence to the photon polarization state to form a quantum key carrier;

[0024] The superconducting nanowire single-photon detector demodulates the carrier polarization information and constructs a BB84 quantum channel;

[0025] When the spectrum main lobe offset exceeds the preset threshold, the quantum key controller starts the sequence update protocol to obtain an updated polarization key sequence;

[0026] The updated polarization key sequence is input into the phase estimation unit of the quantum branch to maintain the stability of the fluctuation characteristic capture.

[0027] Further, the dynamic privacy computing fusioner in the power carbon emission intelligent analysis system based on the double carbon target is configured to:

[0028] The reconfigurable logic gate array receives the polarization key sequence output by the quantum security acquisition engine, executes homomorphic multiplication and addition instructions in parallel through a pipeline architecture, and aggregates the encrypted carbon intensity feature vectors of distributed nodes;

[0029] The quantum key fragment controller obtains the bit error rate data from the quantum channel in real time, generates a Laplace noise vector based on the polarization key sequence, and dynamically adjusts the noise amplitude scaling factor according to the quantization rule:

[0030] The scaling factor=(1-quantum channel bit error rate)×initial privacy budget threshold;

[0031] When the quantum channel bit error rate exceeds the 5% threshold for 3 consecutive periods, the noise model is switched to the Cauchy distribution;

[0032] The differential privacy injection unit superimposes the Laplace noise vector to a feature alignment layer output end of a federated learning gradient tensor;

[0033] The phase estimation unit of the quantum branch monitors the quantum amplitude estimation algorithm standard deviation;

[0034] When the standard deviation exceeds the value of 0.3, the privacy budget controller increases the initial privacy budget threshold by 20%;

[0035] The adjusted noise amplitude input quantum branch non-Gaussian fluctuation encoder is used to ensure the integrity of the fluctuation tail feature.

[0036] Further, the quantum and classical hybrid twin modeling module of the intelligent analysis system for power carbon emissions based on the double carbon target is configured to:

[0037] The classical branch receives the encrypted feature vector output by the dynamic privacy computation fusioner, and constructs a continuous time according to the equation through the gated recurrent unit:

[0038] The input encrypted electricity price time sequence signal is solved by using the adaptive step fourth-order Runge-Kutta method to solve the carbon trajectory differential relationship;

[0039] The quantum branch inputs the encrypted feature vector, prepares a bit superposition state through an RY rotation gate, and extracts a non-Gaussian tail probability amplitude through a quantum phase estimation algorithm;

[0040] The counterfactual gradient instruction of the cross-modal causal audit platform is input into the classical branch to drive dynamic time delay parameter updating;

[0041] The parameter updating direction forms a 90-degree angle with the wave function collapse direction of the quantum phase estimation, which is used to maintain the convergence characteristics of the hybrid modeling topology.

[0042] Further, the multi-agent quantum decision engine of the intelligent analysis system for power carbon emissions based on the double carbon target is configured to:

[0043] The dynamic carbon intensity prediction tensor output by the classical branch and the fluctuation characteristic quantum state output by the quantum branch are input;

[0044] The on-off state of the thermal power unit is encoded as an Ising model spin variable, and the climbing rate constraint of the gas turbine is converted into a transverse magnetic field strength;

[0045] The quantum annealing machine performs a transverse field quenching operation, outputs a Pareto optimal solution set, and a deformation blockchain contract framework generates a dynamic constraint clause:

[0046] When the real-time carbon intensity prediction value exceeds the warning threshold, the standby unit startup penalty coefficient increase amount = 0.2 x the non-Gaussian fluctuation kurtosis value output by the quantum branch;

[0047] When the wind speed turbulence intensity exceeds the set value, the gas turbine minimum output limit is adjusted downward by the amount of the classical branch time lag parameter offset multiplied by a compensation factor of 0.5;

[0048] The compensation factor is dynamically adjusted by the calibration instruction of the cross-modal causal audit platform.

[0049] Further, the power carbon emission intelligent analysis system based on the double carbon target, the cross-modal causal audit platform is configured to:

[0050] The differentiable causal discovery unit receives the scheduling instruction log output by the multi-agent quantum decision engine, and generates a maximum probability causal directed graph through sparse regularization constraint;

[0051] The counterfactual state transition simulator constructs a virtual power plant decision path space:

[0052] Input the current unit combination strategy, simulate the carbon footprint evolution trajectory under different wind and light output scenarios;

[0053] The principle of regret minimization is used to select the alternative strategy with the lowest carbon footprint;

[0054] The gradient backpropagation channel converts the optimization direction of the alternative strategy into a quantum branch rotation gate parameter update;

[0055] The edge weight assignment rule of the causal directed graph is: weight value = quantum branch phase estimation variance x 0.7;

[0056] The selection priority of the alternative strategy is calculated as: priority coefficient = 1 / (dynamic contract penalty coefficient + 0.1).

[0057] Further, the power carbon emission intelligent analysis system based on the double carbon target, the key increment management unit of the data processing module is configured to:

[0058] Divide the data stream block according to a 15-minute time window;

[0059] Based on the cyclic redundancy check code, verify the integrity of the new block, and trigger the quantum key re-distribution protocol when the verification fails;

[0060] The key substitution protocol updates the encryption basis vector of the data slice in the real-time window;

[0061] The vibration spectrum analysis unit monitors the main lobe bandwidth in real time;

[0062] When the main lobe bandwidth > 10kHz, shorten the time window to 5 minutes;

[0063] The quantum key update frequency is set to 1.2 times the refresh rate of the quantum branch phase estimation.

[0064] Further, the double carbon target-based power carbon emission intelligent analysis system disclosed by the application is characterized in that the parameter migration unit is configured to:

[0065] The real-time monitoring module obtains wind and light output data and calculates a real-time output offset;

[0066] The Smirnov test unit compares the real-time output offset with a confidence threshold, and activates the meta-learning optimizer when the offset is greater than the confidence threshold;

[0067] The classical branch full connection layer weight parameters are frozen, and the quantum circuit RX rotation gate angle is optimized by the gradient descent method;

[0068] The cross-modal causal audit platform inputs the edge weight of the causal directed graph into the parameter migration unit;

[0069] The confidence threshold calibration rule is that when the edge weight is greater than 0.5, the confidence threshold is reduced by 20%;

[0070] The quantum circuit optimization frequency is set to 1.1 times the quantum amplitude estimation refresh rate.

[0071] Further, the double carbon target-based power carbon emission intelligent analysis system disclosed by the application is characterized in that the zero-knowledge proof unit is configured to:

[0072] The zero-knowledge proof unit converts the Pareto optimal solution set into an arithmetic circuit satisfiability proof;

[0073] The elliptic curve bilinear pairing verification machine verifies the legality of the unit start-stop strategy;

[0074] The terms are dynamically reconstructed in the deformation blockchain contract framework:

[0075] The carbon footprint constraint range = the kurtosis value of the quantum branch output × 0.8;

[0076] The penalty coefficient verification is based on the classical branch time lag parameter offset reconfiguration: verification threshold = original threshold × (1 + offset);

[0077] The elliptic curve bilinear pairing verification pass rate is calculated as: pass rate = reference value - quantum channel error rate × 0.1;

[0078] When the error rate is greater than 5%, switch to the lattice-based quantum-resistant signature algorithm to ensure auditability.

[0079] Further, the double carbon target-based power carbon emission intelligent analysis system disclosed by the application is characterized in that the feedback channel unit is configured to:

[0080] The optimization direction of the counterfactual replacement strategy is converted into a tensor gradient signal;

[0081] The parameter space of the federated learning aggregator is input through the back propagation link;

[0082] Activation of the noise model switching protocol of the quantum key slice controller

[0083] When the gradient signal amplitude is >0.5, the Cauchy noise distribution is enabled;

[0084] When the gradient signal variance is >0.4, the privacy budget threshold is reduced by 20%;

[0085] The vibration spectrum analysis unit monitors the main lobe bandwidth;

[0086] When the main lobe bandwidth is >12kHz, the gradient signal transmission frequency is set to 2 times the quantum branch phase refresh rate.

[0087] Advantages of the present application

[0088] The present application realizes the spatiotemporal scale complementary analysis of dynamic source load coupling characteristics through the quantum and classical hybrid twin modeling architecture, the classical branch captures the macroscopic continuous dependence relationship of electricity price fluctuation and carbon trajectory, the quantum branch encodes the non-Gaussian fluctuation tail characteristics of wind and light output, and the double-branch collaborative modeling eliminates the representation blind area of single model in the dynamic coupling scene;The dynamic privacy computing fusion mechanism adjusts the noise amplitude based on the polarization key slice strategy, reduces the noise amplitude and switches the Cauchy distribution model when the quantum channel error rate increases, and synchronously maintains the privacy security boundary and the integrity of the non-Gaussian fluctuation characteristics in the ciphertext state federated feature aggregation;The cross-modal causal audit platform constructs a prediction-decision-calibration closed loop path, when the carbon footprint prediction deviation exceeds the statistical threshold, the counterfactual gradient instruction drives the quantum rotation gate parameter calibration and federated feature alignment and reweighting, synchronously triggers the reconstruction of the constraint clauses of the blockchain contract, dynamically corrects the model time lag error caused by the sudden change of source load state, and improves the timeliness and accuracy of low-carbon scheduling of the power system under the double carbon target. BRIEF DESCRIPTION OF DRAWINGS

[0089] In order to more clearly illustrate the present application of the present application, the drawings needed in the embodiments will be briefly introduced below, and obviously, other drawings can be obtained by the drawings without creative labor for ordinary skilled in the art.

[0090] Figure 1 The system architecture diagram of the power carbon emission intelligent analysis system based on the double carbon target provided by the embodiment of the present application. DETAILED DESCRIPTION

[0091] In order to make the purpose of the present application, the present application and the advantages clearer, the present application will be described clearly and completely below in combination with the specific embodiments of the present application and the corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all the other embodiments obtained by the person skilled in the art without making creative efforts belong to the protection scope of the present application. The present application provided by the embodiments of the present application will be described in detail below in combination with the drawings. In order to better understand the purpose of the present application, the present application will be further described in detail below.

[0092] Please refer to Figure 1 The present application provides an intelligent analysis system for power carbon emission based on the double carbon target, comprising:

[0093] The quantum security acquisition engine captures the vibration frequency spectrum of the wind turbine bearing in real time through the photonic crystal optical fiber sensor, simultaneously accesses the millisecond-level electricity price time sequence signal of the electricity spot market API, and analyzes the micro-scale meteorological turbulence tensor issued by the meteorological satellite. The vibration frequency spectrum of the wind turbine bearing is outputted by the Hilbert Huang transform to generate the intrinsic mode function, drive the quantum true random number generator to generate the polarization key sequence, and construct the encrypted data stream resistant to quantum attacks through the decoy state BB84 protocol.

[0094] The dynamic privacy calculation fusioner receives the encrypted data stream, deploys the homomorphic encryption acceleration card to execute the improved federated learning feature alignment algorithm in the ciphertext state, aggregates the carbon intensity feature vector of the distributed node, and controls the amplitude of the Laplace noise injection according to the dynamic sharding strategy of the polarization key sequence.

[0095] The quantum and classical hybrid twin modeling module inputs the encrypted feature vector outputted by the dynamic privacy calculation fusioner, constructs a bifurcated modeling topology, and the bifurcated modeling topology comprises a classical branch and a quantum branch.

[0096] The classical branch adopts the neural differential equation solver to model the continuous time relationship between the electricity price fluctuation and the carbon trajectory, and outputs the dynamic carbon intensity prediction tensor.

[0097] The quantum branch encodes the non-Gaussian fluctuation tail probability amplitude of the wind and light output through the quantum amplitude estimation algorithm, and outputs the fluctuation characteristic quantum state.

[0098] The multi-agent quantum decision engine receives the dynamic carbon intensity prediction tensor and the fluctuation characteristic quantum state, maps the unit commitment optimization problem to the Ising model, and generates the Pareto frontier solution set through quantum annealing optimization.

[0099] Based on the non-Gaussian fluctuation kurtosis value outputted by the quantum branch, the blockchain smart contract constraint clause is dynamically reconstructed according to the real-time carbon prediction value.

[0100] A cross-modal causal auditing platform monitors the carbon footprint boundary of the smart contract constraint clause of the blockchain, locates the fault event causal chain through a differentiable causal graph when the prediction bias exceeds the Smirnov threshold, inputs calibration instructions to the rotation gate parameter space of the quantum branch through the counterfactual gradient backpropagation channel, and synchronously triggers the reconstruction operation of the smart contract constraint clause;

[0101] Wherein: the fragmentation rule of the polarization key sequence is bound with the noise amplitude establishment frequency, and for every 0.1% increase in the quantum channel error rate, the noise amplitude is reduced by 5dB, so that the quantum branch does not trigger privacy leakage when capturing the ±5σ fluctuation characteristics;

[0102] After the counterfactual gradient calibration instruction drives the rotation gate parameter update, it is fed back to the federal learning feature alignment algorithm to reweight the nodes and obtain a closed-loop calibration path for resolving the time lag misalignment of carbon trajectory prediction under dynamic source load coupling conditions.

[0103] Specifically, the power carbon emission intelligent analysis system based on the double carbon target comprises a quantum secure acquisition engine, which constructs an anti-quantum attack encrypted data stream through the following steps:

[0104] The narrow-band photonic crystal fiber sensor deployed in the transmission chain bearing seat captures the wind turbine bearing vibration spectrum data, and converts the Bragg grating reflection wavelength shift into vibration acceleration digital signals;

[0105] The laser Doppler velocimeter obtains the wind speed fluctuation component in real time, and performs matrix correlation on the wind speed fluctuation component and the time sequence turbulence intensity tensor according to the Euclidean distance rule;

[0106] The original meteorological data issued by the meteorological satellite interface is analyzed, and the micro-scale wind direction change probability matrix is extracted;

[0107] The wind turbine bearing vibration spectrum data is processed by Hilbert-Huang transform, and the intrinsic mode function component including frequency and energy distribution is output;

[0108] The quantum true random number generator receives the intrinsic mode function component to generate a polarization encoding basis vector sequence;

[0109] The electro-optic modulator loads the polarization encoding basis vector sequence to the photon polarization state to form a quantum key carrier;

[0110] The superconducting nanowire single-photon detector demodulates the carrier polarization information to construct a BB84 quantum channel;

[0111] When the spectrum main lobe offset exceeds the preset threshold, the quantum key controller starts the sequence update protocol to obtain an updated polarization key sequence;

[0112] The updated polarization key sequence is input into a phase estimation unit of the quantum branch, for maintaining fluctuation characteristic capture stability.

[0113] A quantum secure acquisition engine, a narrow-band photonic crystal fiber sensor of a distributed energy unit physical layer, captures a wind turbine bearing vibration frequency spectrum original signal in real time; a synchronous access power spot market application program interface acquires a millisecond-level electricity price time sequence digital signal; a micro-scale meteorological turbulence tensor data packet issued by a meteorological satellite is parsed; the vibration frequency spectrum original signal is processed by a Hilbert-Huang transform algorithm and decomposed into an intrinsic modal function component containing an inherent frequency and an energy distribution; the component drives a quantum true random number generator to output a polarization encoding base vector sequence; through a BB84 quantum communication protocol, an electro-optical modulator loads the polarization encoding base vector to a photon polarization state to generate a quantum key carrier; a superconducting nanowire single-photon detector demodulates the carrier polarization information and constructs a point-to-point quantum attack-resistant encrypted data transmission channel. This stage realizes strong coupling of physical layer vibration characteristics and quantum key generation, providing a verifiable trust root for dynamic privacy calculation.

[0114] A dynamic privacy calculation fusioner receives the aforementioned quantum attack-resistant encrypted data stream; a reconfigurable logic gate array loads a homomorphic encryption acceleration card hardware driver to execute an improved federated learning feature alignment algorithm in a ciphertext state; carbon intensity feature vectors of distributed nodes in a virtual power plant are aggregated; a quantum key fragmentation controller generates a Laplace noise vector in real time according to the polarization key sequence; the noise amplitude is dynamically adjusted according to the polarization key fragmentation rule; a differential privacy injection unit superimposes the noise vector to the feature alignment layer output port of the federated learning gradient tensor; when the quantum channel transmission quality is abnormal, the noise model is switched to a Cauchy distribution. The noise dynamic scaling mechanism enables the quantum branch to capture non-Gaussian fluctuation characteristics while maintaining the data privacy boundary.

[0115] A quantum and classical hybrid twin modeling module inputs encrypted feature vectors into a bifurcated modeling topology; a classical branch uses a gated recurrent unit to construct a neural ordinary differential equation; the equation inputs encrypted electricity price time sequence signals, models the continuous time dependence relationship between electricity price fluctuations and carbon trajectories through an adaptive step-size Runge-Kutta numerical solution method, and outputs a dynamic carbon intensity prediction tensor; a quantum branch prepares a bit superposition state through a quantum rotation gate; a quantum phase estimation algorithm extracts the non-Gaussian fluctuation tail probability amplitude of wind and light output. The classical branch captures macroscopic trends and the quantum branch analyzes microscopic fluctuations, realizing all-scale analysis of source-load dynamic coupling characteristics.

[0116] A multi-agent quantum decision engine receives a dynamic carbon intensity prediction tensor and a fluctuation characteristic quantum state; maps the start-stop state of thermal power units to Ising model spin variables, and converts the gas turbine climbing rate constraint into a transverse magnetic field strength; a quantum annealing machine performs quenching optimization operations and outputs a Pareto frontier solution set; a metamorphic blockchain contract framework dynamically reconstructs carbon footprint constraint clauses based on the non-Gaussian fluctuation kurtosis value output by the quantum branch; the penalty coefficient of the constraint clause is linearly related to the kurtosis value, eliminating the risk of misstarting of standby units caused by sudden changes in wind and light output.

[0117] A cross-modal causal audit platform monitors the carbon footprint boundaries of blockchain smart contract constraint clauses; when the prediction deviation exceeds the statistical test threshold, the differentiable causal discovery algorithm analyzes the dispatch instruction log to generate a causal directed graph; a counterfactual state transition simulator constructs a virtual power plant decision path space and selects the optimal alternative strategy using the principle of regret minimization; a gradient backpropagation channel converts the strategy optimization direction into a quantum rotation gate parameter calibration quantity; and the calibration instruction reweights the nodes through a federated learning feature alignment algorithm to form a closed-loop feedback path of modeling-decision-auditing.

[0118] The polarization key fragment rule and the noise amplitude establish a frequency response binding, which automatically reduces the noise amplitude when the quantum channel error rate increases, ensuring the privacy security of the fluctuation characteristic capture of the quantum branch; after the counterfactual gradient instruction drives the rotation gate parameter update, it is fed back to the federated learning feature alignment algorithm to adjust the node weight, and through the continuous calibration of the dynamic source-load coupling feature, the time lag bias of the carbon emission intensity prediction is eliminated. The quantum key controller, the federated aggregator and the quantum rotation gate form a triangular collaborative architecture, realizing the trusted data flow from the physical layer to the decision layer.

[0119] Specifically, the power carbon emission intelligent analysis system based on the double carbon target disclosed in the present application, the dynamic privacy computing fusion device is configured to:

[0120] The reconfigurable logic gate array receives the polarization key sequence output by the quantum secure acquisition engine, executes the homomorphic multiplication and addition instruction in parallel through the pipeline architecture, and aggregates the encrypted carbon intensity feature vector of the distributed nodes;

[0121] The quantum key fragment controller obtains error rate data from the quantum channel in real time, generates a Laplace noise vector based on the polarization key sequence, and dynamically adjusts the noise amplitude scaling factor according to the quantization rule:

[0122] The scaling factor = (1- quantum channel error rate) x initial privacy budget threshold;

[0123] When the quantum channel error rate exceeds the 5% threshold for 3 consecutive periods, switch the noise model to the Cauchy distribution;

[0124] The differential privacy injection unit superimposes the Laplace noise vector to the feature alignment layer output end of the federated learning gradient tensor;

[0125] The quantum branch phase estimation unit monitors the quantum amplitude estimation algorithm standard deviation;

[0126] When the standard deviation exceeds the 0.3 value, the privacy budget controller increases the initial privacy budget threshold by 20%;

[0127] The adjusted noise amplitude input quantum branch non-Gaussian fluctuation encoder is used to ensure the integrity of the fluctuation tail feature.

[0128] The reconfigurable logic gate array receives the polarization key sequence transmitted by the quantum secure acquisition engine, which serves as the encryption basis for the homomorphic multiplication and addition instruction. Under the driving of the pipeline architecture, the array processes the encrypted carbon intensity feature vectors of distributed nodes in parallel, realizes the ciphertext feature aggregation operation, and completes the privacy-protected fusion of multi-source data.

[0129] The quantum key fragment controller monitors the quantum channel transmission quality in real time and captures the quantum bit error rate change data. Based on the physical entropy source characteristics of the polarization key sequence, the controller generates a Laplace noise vector that meets the differential privacy requirement. The noise amplitude scaling factor dynamically responds to the quantum channel error rate fluctuation, forming a negative feedback regulation mechanism of noise intensity and transmission quality.

[0130] When the quantum channel transmission quality is below the reliability threshold for a continuous number of periods, the system automatically switches the noise model to the Cauchy probability distribution. This model switching mechanism enhances the computational robustness under extreme channel conditions and avoids data distortion in the feature aggregation process due to quantum interference.

[0131] The differential privacy injection unit implements noise intervention during the federated learning gradient update phase. This unit accurately superimposes the Laplace noise vector to the feature alignment layer output end, so that the feature gradient is always within the differential privacy protection boundary during the backpropagation process, preventing the original data features from being leaked during the aggregation process.

[0132] The quantum branch phase estimation unit synchronously monitors the stability indicators of the quantum amplitude calculation process. When the algorithm output fluctuation exceeds the set threshold, it indicates that the quantum feature extraction process is at risk of insufficient privacy protection strength, at which time the privacy budget threshold adjustment mechanism is triggered.

[0133] The privacy budget controller increases the initial privacy budget threshold based on the monitoring results, enhancing the mathematical constraint strength of noise injection. This control operation directly affects the calculation process of the noise amplitude scaling factor, forming a closed-loop debugging mechanism for algorithm stability and privacy protection strength.

[0134] The adjusted noise amplitude is output to a quantum branch non-Gaussian fluctuation encoder. The encoder extracts the tail feature of the wind and light output within the noise protection boundary, maintains the fluctuation mode recognition accuracy, meets the differential privacy requirement, and realizes the dynamic balance of feature utility and privacy protection. The whole process constitutes a complete technical closed loop from quantum channel monitoring to noise parameter adjustment and feature protection implementation.

[0135] Specifically, the quantum and classical hybrid twin modeling module of the power carbon emission intelligent analysis system based on the double carbon target is configured to:

[0136] The classical branch receives the encrypted feature vector output by the dynamic privacy computing fusioner, and constructs a continuous time equation according to the equation through a gated recurrent unit.

[0137] The input encrypted electricity price time sequence signal is solved by using the adaptive step fourth-order Runge-Kutta method to solve the carbon trajectory differential relationship;

[0138] The quantum branch inputs the encrypted feature vector, prepares a bit superposition state through an RY rotation gate, and extracts a non-Gaussian tail probability amplitude through a quantum phase estimation algorithm;

[0139] The counterfactual gradient instruction of the cross-modal causal audit platform is input into the classical branch to drive dynamic time delay parameter updating;

[0140] The parameter updating direction and the wave function collapse direction of the quantum phase estimation form a 90-degree angle, which is used to maintain the convergence characteristics of the hybrid modeling topology.

[0141] The classical branch receives the encrypted feature vector transmitted by the dynamic privacy computing fusioner, and the vector contains privacy-protected features associated with the price and carbon intensity. The gated recurrent unit constructs a continuous time dependent equation, and models the differential action relationship of price fluctuation on carbon trajectory by inputting an encrypted electricity price time sequence signal. The adaptive step control mechanism dynamically adjusts the fourth-order Runge-Kutta numerical integration solution process, accurately captures the continuous carbon trajectory evolution law under the condition of dynamic source load coupling.

[0142] The quantum branch processes the homologous encrypted feature vector in parallel, and prepares a bit superposition state through a quantum rotation gate operation. The quantum state encodes the wind and light output probability amplitude distribution characteristics, the quantum phase estimation algorithm extracts the tail feature of the non-Gaussian fluctuation in the probability space, and quantifies the microscopic impact effect of wind and light random output on carbon emission intensity. The classical branch captures the macro trend and the quantum branch analyzes the microscopic fluctuation to form a complementary modeling structure.

[0143] The counterfactual gradient instruction generated by the cross-modal causal auditing platform inputs the classical branch parameter space. The instruction contains carbon footprint correction information under the virtual power plant scheduling strategy optimization path, which drives the dynamic time delay parameter update mechanism to start. The parameter update direction is determined by the mathematical vector direction of the gradient instruction, forming the dynamic calibration capability of the conduction delay effect of the electricity market electricity price signal.

[0144] The dynamic time delay parameter update direction and the quantum branch wave function collapse direction establish an orthogonal geometric constraint. The orthogonal relationship forms a fixed ninety-degree spatial angle, avoiding the optimization goal conflict between the classical differential equation solving process and the quantum probability amplitude calculation. The orthogonal constraint maintains the mathematical convergence stability of the hybrid modeling topology, ensuring the collaborative efficiency of classical analysis and quantum probability calculation. The whole process constitutes a complete modeling closed loop from feature input, double branch collaboration to audit feedback.

[0145] Specifically, the power carbon emission intelligent analysis system based on the double carbon target, the multi-agent quantum decision engine is configured to:

[0146] Input the dynamic carbon intensity prediction tensor output by the classical branch and the fluctuation characteristic quantum state output by the quantum branch;

[0147] The start-stop state of the thermal power unit is coded as an Ising model spin variable, and the climbing rate constraint of the gas turbine is converted into a transverse magnetic field strength;

[0148] The quantum annealing machine performs a transverse field quenching operation, outputs a set of Pareto optimal solutions, and a deformation blockchain contract framework generates dynamic constraint clauses:

[0149] When the real-time carbon intensity prediction value exceeds the warning threshold, the standby unit startup penalty coefficient increase amount = 0.2 x non-Gaussian fluctuation kurtosis value of quantum branch output;

[0150] When the wind speed turbulence intensity exceeds the set value, the gas turbine minimum output limit downshift amount = classical branch time delay parameter offset amount x compensation coefficient 0.5;

[0151] The compensation coefficient is dynamically adjusted by the calibration instruction of the cross-modal causal auditing platform.

[0152] The multi-agent quantum decision engine receives the dynamic carbon intensity prediction tensor output by the classical branch and the fluctuation characteristic quantum state output by the quantum branch. The prediction tensor encapsulates the dynamic correlation law of electricity price and carbon emission, and the quantum state carries the non-Gaussian fluctuation characteristics of wind and light output. The input data stream is synchronously conducted to the quantum optimization solving layer and the constraint rule generation layer, supporting multi-dimensional technical collaboration in the decision-making process.

[0153] The quantum optimization solving layer maps the start-stop state of the thermal power unit to the spin variable parameter of the Ising model, and converts the climbing rate constraint of the gas turbine into the transverse magnetic field strength parameter. The quantum annealing machine performs a transverse field quenching operation, searches for the optimal solution space through the evolution path of the time-dependent Schrodinger equation, and outputs a Pareto frontier solution set that satisfies the multi-objective balance. The solution set reflects the dual requirements of economy and low carbon of unit combination.

[0154] The deformation blockchain contract framework generates dynamic constraint clauses based on the non-Gaussian wave fluctuation kurtosis value of the quantum branch. When the real-time carbon intensity prediction value exceeds the system warning threshold, a backup unit startup penalty mechanism is triggered, and the penalty coefficient increases at a linear proportional rate to the kurtosis value, which quantifies the impact strength of wind and light output on backup capacity. The constraint clauses are guaranteed to be rigid through the tamper-proof nature of the blockchain smart contract.

[0155] When the wind speed turbulence intensity exceeds the set safety limit, the minimum output limit of the gas turbine is dynamically adjusted downward. The adjustment amount is calculated based on the time delay parameter offset of the classical branch model, which reflects the degree of lag in model prediction under sudden changes in wind and light. The compensation factor, as a key adjustment variable, receives real-time calibration instructions from the cross-modal causal audit platform, forming a decision error correction mechanism.

[0156] The compensation factor of the constraint parameter establishes a dynamic adjustment mechanism. The audit platform monitors the deviation characteristics of actual carbon emissions and predicted values, and generates calibration instructions through gradient counterfactual analysis. The instructions are injected into the compensation factor calculation process, achieving real-time adaptation of decision constraints and source-load state changes. The whole process constitutes a closed-loop decision path from prediction data input, quantum optimization solving, dynamic constraint generation, to audit feedback.

[0157] Specifically, the intelligent analysis system for power carbon emissions based on the dual-carbon target, the cross-modal causal audit platform is configured to:

[0158] The differentiable causal discovery unit receives the scheduling instruction log output by the multi-agent quantum decision engine, and generates a maximum probability causal directed graph through sparse regularization constraints;

[0159] The counterfactual state transition simulator constructs a virtual power plant decision path space:

[0160] Input the current unit combination strategy, simulate the carbon footprint evolution trajectory under different wind and light output scenarios;

[0161] Adopt the principle of regret minimization to select the alternative strategy with the lowest carbon footprint;

[0162] The gradient backpropagation channel converts the optimization direction of the alternative strategy into the parameter update amount of the quantum branch rotation gate;

[0163] The edge weight assignment rule of the causal directed graph is: weight value = quantum branch phase estimation variance * 0.7.

[0164] The selection priority of the alternative strategy is calculated as: priority coefficient = 1 / (dynamic contract penalty coefficient + 0.1).

[0165] The differentiable causal discovery unit receives the scheduling instruction log data stream transmitted by the multi-agent quantum decision engine. The log event sequence is analyzed by using a sparse regularization constraint algorithm, the causal strength relationship between variables is automatically learned through a differentiable architecture, and a causal directed topological graph with maximum likelihood is generated. The topological graph reveals the conduction link between price fluctuations and carbon trajectory anomalies, laying the foundation for fault diagnosis.

[0166] The counterfactual state transition simulator constructs a multi-dimensional decision path space of a virtual power plant. Input the current unit combined scheduling strategy parameters, simulate the carbon footprint evolution trajectory under different random scenarios according to the wind and light output probability distribution. The total carbon emissions of each strategy path are evaluated using the regret minimization optimization principle, and the alternative scheduling scheme with the lowest carbon footprint is selected.

[0167] The gradient backpropagation channel converts the optimization direction information of the alternative strategy into a tensor signal. The signal is mapped to the rotation gate angle adjustment amount by the quantum differential processor, and input into the quantum branch parameter space to implement model calibration. The parameter update responds to the carbon footprint optimization demand, and eliminates the deviation effect of the decision scheme and the actual carbon emission.

[0168] The edge weight assignment of the causal directed graph responds to the quantum phase estimation variance index. The variance value reflects the uncertainty degree of quantum amplitude calculation, and the edge weight value and the variance index establish a fixed proportional factor relationship, so that the causal graph structure dynamically evolves with the stability of quantum calculation.

[0169] The calculation of the selection priority of the alternative strategy is associated with the dynamic contract penalty parameter. The priority coefficient and the penalty coefficient establish an inverse compensation mechanism, which automatically improves the retrieval level of the alternative strategy when the contract clause strengthens the punishment, forming a mutual adaptation relationship between the constraint condition and the strategy optimization.

[0170] The audit result triggers two key operations through the feedback link in synchronization: sending the rotation gate parameter calibration instruction to the quantum modeling module; and initiating the contract clause reconstruction request to the decision engine. The double-path cooperative mechanism opens up the whole-process closed loop from anomaly diagnosis to model correction to decision improvement.

[0171] Specifically, the power carbon emission intelligent analysis system based on the double carbon target disclosed in the application, the key increment management unit of the data processing module is configured to:

[0172] Divide the data stream block according to a 15-minute time window;

[0173] The new block integrity is verified based on a cyclic redundancy check code, and a quantum key re-distribution protocol is triggered when the verification fails;

[0174] The key replacement protocol updates the encryption basis vector of the data slice in the real-time window;

[0175] The vibration spectrum analysis unit monitors the main lobe bandwidth in real time;

[0176] When the main lobe bandwidth is greater than 10 kHz, the time window is shortened to 5 minutes;

[0177] The quantum key update frequency is set to 1.2 times the quantum branch phase estimation refresh rate.

[0178] The data stream is divided into processing blocks according to a fixed time period, and the block time boundary is aligned with the system clock reference. The block division unit uses a fixed time window mechanism to manage the data stream, and the window period is divided into time slots according to the system clock establishment standard to form a periodic data processing framework.

[0179] The cyclic redundancy check module verifies the integrity of the newly generated data block. The check module calculates the redundancy check code value of the block data, compares it with the preset check reference value to determine the transmission integrity. When a check failure event occurs, a quantum key re-distribution security protocol is triggered to ensure that the data stream conversion process is not tampered with.

[0180] The key replacement protocol updates the encryption basis vector of the data slice in the current time window. The protocol only replaces the polarization key sequence slice of the specific window block, realizes the fine-grained rolling update of the key resource, and avoids the calculation burden brought by the full amount of key distribution.

[0181] The vibration spectrum monitoring unit continuously acquires the physical indicators of the fan bearing main lobe bandwidth. The unit obtains the frequency domain distribution characteristics of the vibration energy through a photonic crystal fiber sensor, and the main lobe bandwidth is used as a key transmission indicator to reflect the frequency spectrum energy concentration characteristics.

[0182] The time window adjustment unit responds to the change state of the main lobe bandwidth. When it is monitored that the main lobe bandwidth exceeds the system set threshold value, the time window period will be dynamically reduced to a shorter time slot, improving the real-time and sensitivity of data slice division.

[0183] The quantum key update frequency establishes a cooperative mechanism with the quantum branch calculation unit. The key management unit receives the phase refresh rate signal of the quantum amplitude estimation process, and sets the key update frequency to a fixed multiple of the refresh rate. The frequency binding design makes the encryption update rhythm adapt to the quantum calculation period, and maintains the stability of the non-Gaussian fluctuation feature capture.

[0184] Specifically, the power carbon emission intelligent analysis system based on the double carbon target is configured as:

[0185] The real-time monitoring module obtains wind and light output data and calculates a real-time output offset;

[0186] The Smirnov test unit compares the real-time output offset with a confidence threshold, and activates the meta-learning optimizer when the offset > the confidence threshold;

[0187] The classical branch fully connected layer weight parameters are frozen, and the RX rotation gate angle of the quantum circuit is optimized by the gradient descent method;

[0188] The edge weight of the causal directed graph of the cross-modal causal audit platform is input into the parameter migration unit;

[0189] The confidence threshold calibration rule is that when the edge weight > 0.5, the confidence threshold is reduced by 20%;

[0190] The quantum circuit optimization frequency is set to 1.1 times the quantum amplitude estimation refresh rate.

[0191] The real-time monitoring module continuously collects wind farm and photovoltaic power station output data streams. Through a dynamic comparison algorithm of historical output curve and current measured value, the real-time output offset value of wind and light power sources is calculated. The offset quantifies the deviation degree of the actual output from the predicted benchmark value, reflecting the dynamic fluctuation state of the source and load.

[0192] The Smirnov test unit receives real-time output offset data. The test unit performs statistical distribution matching test on the offset value and the system confidence threshold parameter. When the test result confirms that the offset exceeds the upper limit of the confidence interval, the start signal of the meta-learning optimizer is activated, triggering the model parameter migration mechanism.

[0193] The meta-learning optimizer performs double operations in response to the start signal: freezes all weight parameters of the classical branch fully connected layer to maintain the stability of the macro modeling structure; and only optimizes the angle parameters of the RX rotation gate in the quantum circuit through the gradient descent algorithm. This mechanism realizes the dynamic fine-tuning ability of the quantum branch.

[0194] The cross-modal causal audit platform inputs the edge weight data stream of the causal directed graph into the parameter migration unit. The edge weight value reflects the causal strength of the fault event and serves as the calibration basis for the model confidence. The parameter migration unit has a built-in confidence threshold calibration mechanism. When the edge weight value exceeds the set threshold, the value reduction program of the confidence threshold is automatically started.

[0195] The quantum circuit optimization frequency setting unit establishes a cooperative mechanism with the quantum branch. The unit obtains the phase refresh rate parameter of the quantum amplitude estimation process and sets the parameter optimization frequency to a fixed multiple of the phase refresh rate. The frequency binding design maintains the synchronization of the quantum parameter optimization period and the feature extraction period, enhancing the stability of the system.

[0196] Specifically, the double-carbon-target-based power carbon emission intelligent analysis system disclosed by the application is characterized in that the zero-knowledge proof unit is configured to:

[0197] The zero-knowledge proof unit converts the Pareto optimal solution set into an arithmetic circuit satisfiability proof.

[0198] The elliptic curve bilinear pairing verifier verifies the legality of the unit start-stop strategy.

[0199] The deformation blockchain contract framework dynamically reconstructs the carbon footprint constraint range.

[0200] The carbon footprint constraint range = the kurtosis value of the quantum branch output * 0.8.

[0201] The penalty coefficient verification is reconfigured according to the time lag parameter offset of the classical branch, that is, the verification threshold = the original threshold * (1 + offset).

[0202] The elliptic curve bilinear pairing verification pass rate is calculated as: pass rate = benchmark value - quantum channel error rate * 0.1.

[0203] When the error rate is greater than 5%, switch to the lattice-based quantum-resistant signature algorithm to ensure auditability.

[0204] The zero-knowledge proof unit converts the Pareto optimal solution set output by the multi-agent decision engine into an arithmetic circuit logic structure. The structure encapsulates the mathematical constraint conditions of the unit start-stop strategy, expresses the feasibility of the decision scheme through the combination relationship of Boolean circuits and arithmetic gates. The proof generation process hides the specific strategy parameters but preserves the verifiable characteristics.

[0205] The elliptic curve bilinear pairing verifier verifies the legality of the decision strategy. Based on the bilinear mapping characteristics of elliptic curve cryptography, the verifier verifies whether the unit combination strategy satisfies the polynomial constraint relationship in the arithmetic circuit, outputs a binary verification result, and maintains commercial confidentiality zero leakage.

[0206] The deformation blockchain contract framework dynamically reconstructs the carbon footprint constraint range. The constraint range value responds in real time to the non-Gaussian fluctuation kurtosis parameter of the quantum branch output, and the two maintain a fixed multiple ratio relationship. The change of the kurtosis value automatically triggers the contract boundary adjustment, enhancing the adaptability of the constraint mechanism to wind and light fluctuation scenarios.

[0207] The penalty coefficient verification mechanism is dynamically reconfigured according to the time lag parameter offset of the classical branch. The original verification threshold is proportionally scaled according to the time lag offset, and the offset reflects the lag degree of the carbon trajectory prediction. This mechanism converts the model accuracy loss into a verification fault tolerance space to maintain the validity of the contract.

[0208] The bilinear pairing verification pass rate is negatively correlated with the quantum channel error rate.

[0209] When the quantum channel error rate continuously exceeds the system security threshold, the zero-knowledge proof unit automatically switches to a lattice-based quantum-resistant signature algorithm.

[0210] Specifically, the power carbon emission intelligent analysis system based on the double carbon target is configured with a feedback channel unit.

[0211] The optimization direction of the counterfactual replacement strategy is converted into a tensor gradient signal.

[0212] The parameter space of the federated learning aggregator is input through the backpropagation link.

[0213] The noise model switching protocol of the quantum key fragment controller is activated.

[0214] When the gradient signal amplitude is greater than 0.5, the Cauchy noise distribution is enabled.

[0215] When the gradient signal variance is greater than 0.4, the privacy budget threshold is reduced by 20%.

[0216] The vibration spectrum analysis unit monitors the main lobe bandwidth.

[0217] When the main lobe bandwidth is greater than 12kHz, the gradient signal transmission frequency is set to twice the quantum branch phase refresh rate.

[0218] The feedback channel unit receives the counterfactual replacement strategy transmitted by the cross-modal causal audit platform. The strategy contains virtual power plant low-carbon scheduling optimization direction parameters, which are encoded into a tensor gradient signal structure by the conversion module. The signal structure retains the mathematical guiding features of the counterfactual strategy, making the decision optimization intention quantifiable.

[0219] The gradient signal is input into the parameter space of the federated learning aggregator through the backpropagation link. This link establishes a direct channel between the audit platform and the privacy computing module. The gradient tensor acts on the weight update process of the feature alignment layer, integrating carbon footprint optimization requirements into the federated feature aggregation mechanism.

[0220] The quantum key slice controller activates the noise model switching protocol.The protocol responds to the change of gradient signal eigenvalue: when the signal amplitude exceeds the system set threshold, the Cauchy noise distribution model is enabled; when the signal variance index breaks through the system tolerance boundary, the privacy budget threshold value is reduced. This switching mechanism dynamically balances the feature protection strength and model optimization demand.

[0221] The vibration spectrum analysis unit continuously monitors the fan bearing main lobe bandwidth index. The unit analyzes the vibration frequency domain data collected by the photonic crystal fiber sensor, and the main lobe bandwidth physical parameter reflects the mechanical vibration energy concentration characteristic, which is used as the basis for real-time adjustment of the system.

[0222] The gradient signal transmission frequency control unit responds to the change of spectrum characteristics. When the main lobe bandwidth exceeds the system reliability threshold value, the transmission frequency is automatically adjusted to a fixed multiple relationship of the quantum branch phase refresh rate. The multiple design makes the signal update rhythm match the quantum calculation period, and maintains the stability of the non-Gaussian fluctuation feature capture.

[0223] The present application solves the problem of prediction inaccuracy by constructing a quantum and classical hybrid twin modeling architecture. The classical branch uses neural differential equation modeling to model the continuous time dependence of electricity price fluctuations and carbon trajectory, capturing macro trend characteristics; the quantum branch encodes the non-Gaussian fluctuation tail probability amplitude of wind and light output through quantum amplitude estimation algorithm, analyzing micro fluctuation characteristics. The parallel processing of the double branches forms a time-space scale complement, realizing the holographic analysis of source-load dynamic coupling characteristics.

[0224] The system establishes a dynamic privacy calculation fusion mechanism to ensure the accuracy of feature extraction. Based on the polarization key sequence slicing strategy, the amplitude of Laplace noise injection is dynamically controlled, and the noise scaling factor is adaptively adjusted according to the quantum channel error rate. When the channel quality deteriorates, automatically switch to Cauchy noise distribution, maintain the effectiveness of the federated learning feature alignment algorithm in the ciphertext state, so that the aggregated carbon intensity feature vector has both privacy security and dynamic adaptability.

[0225] The cross-modal causal audit platform forms a closed-loop calibration path. When the prediction deviation exceeds the statistical test threshold, the fault event causal chain is located through the differentiable causal graph, and the calibration instruction is input to the quantum branch rotation gate parameter space through the counterfactual gradient backpropagation channel. The instruction synchronously triggers the reconstruction of the smart contract constraint clause, and feeds back the parameter update to the federated learning feature alignment algorithm re-weighted node, realizing the cooperative optimization of model parameters and decision rules, and continuously correcting the prediction time lag caused by dynamic source-load coupling.

Claims

1. The intelligent analysis system of power carbon emissions based on dual carbon goals is characterized by: include: The quantum security acquisition engine uses photonic crystal fiber sensors to capture the wind turbine bearing vibration spectrum in real time. It also accesses the electricity spot market API to obtain millisecond-level electricity price timing signals and analyzes the microscale meteorological turbulence tensor transmitted by meteorological satellites. The wind turbine bearing vibration spectrum is then transformed into an intrinsic mode function using the Hilbert-Huang transform. This function is then used to drive a quantum true random number generator to generate a polarization key sequence. This process then constructs a quantum-resistant encrypted data stream using the decoyed state BB84 protocol. A dynamic privacy computing aggregator receives the encrypted data stream, deploys a homomorphic encryption accelerator card to execute an improved federated learning feature alignment algorithm in a ciphertext state, aggregates the carbon intensity feature vectors of distributed nodes, and controls the Laplace noise injection amplitude according to the dynamic sharding strategy of the polarization key sequence; The quantum and classical hybrid twin modeling module inputs the encrypted feature vector output by the dynamic privacy computing fusion module and constructs a bifurcated modeling topology. The bifurcated modeling topology includes classical branches and quantum branches: The classical branch uses a neural differential equation solver to model the continuous-time relationship between electricity price fluctuations and carbon trajectories, and outputs a dynamic carbon intensity prediction tensor; The quantum branch encodes the probability amplitude of the non-Gaussian fluctuation tail of wind and solar power output through a quantum amplitude estimation algorithm and outputs the quantum state of the fluctuation characteristics; A multi-agent quantum decision engine receives the dynamic carbon intensity prediction tensor and the quantum state of the fluctuation characteristic, maps the unit commitment optimization problem to the Ising model, and generates a Pareto frontier solution set through quantum annealing optimization; Based on the non-Gaussian fluctuation kurtosis value of the quantum branch output, a blockchain smart contract constraint clause is generated that is dynamically reconstructed according to the real-time carbon forecast value; A cross-modal causal audit platform monitors the carbon footprint boundaries of the blockchain smart contract constraints. When the prediction deviation exceeds the Smirnov threshold, it locates the causal chain of the fault event through a differentiable causal graph. Through the counterfactual gradient backpropagation channel, it inputs calibration instructions into the revolving door parameter space of the quantum branch, synchronously triggering the reconstruction operation of the smart contract constraints. The sharding rule of the polarization key sequence establishes frequency binding with the noise amplitude. For every 0.1% increase in the quantum channel bit error rate, the noise amplitude is reduced by 5dB, so that the quantum branch captures the ±5σ fluctuation characteristics without triggering privacy leakage. The counterfactual gradient calibration instruction drives the revolving door parameter update and then feeds back to the federated learning feature alignment algorithm reweighting node to obtain a closed-loop calibration path, which is used to resolve the time lag inaccuracy of carbon trajectory prediction under dynamic source-load coupling conditions.

2. The intelligent analysis system for power carbon emissions based on dual carbon goals according to claim 1 is characterized in that: The quantum-safe acquisition engine constructs an encrypted data stream that is resistant to quantum attacks through the following steps: A narrowband photonic crystal fiber sensor deployed in the drive chain bearing seat captures the wind turbine bearing vibration spectrum data and converts the Bragg grating reflection wavelength offset into a vibration acceleration digital signal; The laser Doppler velocimeter acquires wind speed fluctuation components in real time, and performs matrix correlation between the wind speed fluctuation components and the time series turbulence intensity tensor according to the Euclidean distance rule; Analyze the raw meteorological data sent by the meteorological satellite interface and extract the microscale wind direction change probability matrix; The fan bearing vibration spectrum data is processed by Hilbert-Huang transform to output intrinsic mode function components including frequency and energy distribution; A quantum true random number generator receives the intrinsic mode function component and generates a polarization coding basis vector sequence; The electro-optical modulator loads the polarization coding basis vector sequence into the photon polarization state to form a quantum key carrier; Superconducting nanowire single-photon detectors demodulate carrier polarization information to construct BB84 quantum channels; When the spectrum main lobe offset exceeds the preset threshold, the quantum key controller starts the sequence update protocol to obtain the updated polarization key sequence; The updated polarization key sequence is input into the phase estimation unit of the quantum branch to maintain the stability of wave characteristic capture.

3. The intelligent analysis system for power carbon emissions based on dual carbon goals according to claim 2 is characterized in that: The dynamic privacy computing aggregator is configured to: The reconfigurable logic gate array receives the polarization key sequence output by the quantum security acquisition engine, executes homomorphic multiplication and addition instructions in parallel through a pipeline architecture, and aggregates the encrypted carbon intensity feature vectors of distributed nodes; The quantum key sharding controller obtains bit error rate data from the quantum channel in real time, generates a Laplace noise vector based on the polarization key sequence, and dynamically adjusts the noise amplitude scaling factor according to the quantization rule: Scaling factor = (1-quantum channel bit error rate) × initial privacy budget threshold; When the quantum channel bit error rate exceeds the 5% threshold for three consecutive cycles, the noise model is switched to the Cauchy distribution; The differential privacy injection unit superimposes the Laplace noise vector onto the output end of the feature alignment layer of the federated learning gradient tensor; The phase estimation unit of the quantum branch monitors the standard deviation of the quantum amplitude estimation algorithm; When the standard deviation exceeds 0.3, the privacy budget controller increases the initial privacy budget threshold by 20%; The adjusted noise amplitude is input into the quantum branch non-Gaussian fluctuation encoder to ensure the integrity of the fluctuation tail characteristics.

4. The intelligent analysis system for power carbon emissions based on dual carbon targets according to claim 3 is characterized in that: The quantum and classical hybrid twin modeling module is configured to: The classic branch receives the encrypted feature vector output by the dynamic privacy computing fusion unit and constructs the continuous time according to the equation through the gated recurrent unit: The encrypted electricity price time series signal is input and the adaptive step-size fourth-order Runge-Kutta method is used to solve the differential relationship of the carbon trajectory; The quantum branch inputs the encrypted feature vector, prepares the bit superposition state through the RY rotation gate, and extracts the non-Gaussian tail probability amplitude through the quantum phase estimation algorithm; The counterfactual gradient instructions of the cross-modal causal audit platform input the classical branch to drive the dynamic time-delay parameter update; The parameter update direction forms a 90-degree angle with the wave function collapse direction of the quantum phase estimation, which is used to maintain the topological convergence characteristics of the hybrid modeling.

5. The intelligent analysis system for power carbon emissions based on dual carbon goals according to claim 4 is characterized in that: The multi-agent quantum decision engine is configured to: Inputting the dynamic carbon intensity prediction tensor output by the classical branch and the wave characteristic quantum state output by the quantum branch; The start and stop states of the thermal power units are encoded as Ising model spin variables, and the gas turbine ramp rate constraint is converted into the transverse magnetic field intensity. The quantum annealing machine performs transverse field quenching operations, outputs a Pareto optimal solution set, and transforms the blockchain contract framework to generate dynamic constraint clauses: When the real-time carbon intensity forecast value exceeds the warning threshold, the penalty coefficient for the standby unit startup is increased by 0.2 × the non-Gaussian fluctuation kurtosis value of the quantum branch output; When the wind speed turbulence intensity exceeds the set value, the gas turbine minimum output limit reduction amount = classic branch time lag parameter offset × compensation coefficient 0.5; The compensation coefficient is dynamically adjusted by the calibration instructions of the cross-modal causal audit platform.

6. The intelligent analysis system for power carbon emissions based on dual carbon goals according to claim 5 is characterized in that: The cross-modal causal auditing platform is configured to: A differentiable causal discovery unit receives the scheduling instruction log output by the multi-agent quantum decision engine and generates a maximum probability causal directed graph through sparse regularization constraints; The counterfactual state transition simulator constructs the virtual power plant decision path space: Input the current unit combination strategy to simulate the carbon footprint evolution trajectory under different wind and solar output scenarios; Adopt the regret minimization principle to select the alternative strategy with the lowest carbon footprint; The gradient back propagation channel converts the optimization direction of the alternative strategy into the parameter update amount of the quantum branch rotation gate; The edge weight assignment rule of the causal directed graph is: weight value = quantum branch phase estimation variance × 0.7; The selection priority of the alternative strategy is calculated as: priority coefficient = 1 / (dynamic contract penalty coefficient + 0.1).

7. The intelligent analysis system for power carbon emissions based on dual carbon targets according to claim 6 is characterized in that: The key increment management unit of the data processing module is configured as follows: Divide the data stream into blocks according to 15-minute time windows; Verify the integrity of the new block based on cyclic redundancy check codes, and trigger the quantum key redistribution protocol when the verification fails; The key replacement protocol updates the encryption basis of the data slice within the real-time window; The vibration spectrum analysis unit monitors the main lobe bandwidth in real time; When the main lobe bandwidth is greater than 10kHz, the time window is shortened to 5 minutes; The quantum key update frequency is set to 1.2 times the quantum branch phase estimation refresh rate.

8. The intelligent analysis system for power carbon emissions based on dual carbon targets according to claim 7 is characterized in that: The parameter migration unit is configured to: The real-time monitoring module obtains wind and solar power output data and calculates the real-time output offset; The Smirnov test unit compares the real-time output offset with a confidence threshold, and activates the meta-learning optimizer when the offset is greater than the confidence threshold; Freeze the weight parameters of the classical branch fully connected layer and optimize the RX revolving gate angle of the quantum circuit by gradient descent method; The edge weights of the causal directed graph of the cross-modal causal audit platform are input into the parameter migration unit; The confidence threshold calibration rule is: when the edge weight is > 0.5, the confidence threshold is reduced by 20%; The quantum circuit optimization frequency is set to 1.1 times the quantum amplitude estimation refresh rate.

9. The power carbon emission intelligent analysis system based on dual carbon goals according to claim 8 is characterized in that: The zero-knowledge proof unit is configured to: The zero-knowledge proof unit converts the Pareto optimal solution set into an arithmetic circuit satisfiability proof; Verify the legitimacy of the unit start-stop strategy through elliptic curve bilinear pairing; Dynamically reconstructing terms in a deformable blockchain contract framework: Carbon footprint constraint range = kurtosis of quantum branch output × 0.8; The penalty coefficient verification is reconfigured based on the offset of the classic branch delay parameter as follows: verification threshold = original threshold × (1 + offset); The pass rate of the elliptic curve bilinear pairing verification is calculated as follows: pass rate = reference value - quantum channel bit error rate × 0.1; When the bit error rate is greater than 5%, it switches to a lattice-based quantum-resistant signature algorithm to ensure auditability.

10. The power carbon emission intelligent analysis system based on dual carbon goals according to claim 9 is characterized in that: The feedback channel unit is configured as follows: Convert the optimization direction of the counterfactual alternative strategy into a tensor gradient signal; Input the parameter space of the federated learning aggregator through the back-propagation link; Noise model switching protocol for activating quantum key sharding controllers: When the gradient signal amplitude is greater than 0.5, the Cauchy noise distribution is enabled; When the gradient signal variance is greater than 0.4, the privacy budget threshold is reduced by 20%; The vibration spectrum analysis unit monitors the main lobe bandwidth; When the main lobe bandwidth is greater than 12 kHz, the gradient signal transmission frequency is set to twice the quantum branch phase refresh rate.

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