Park carbon emission monitoring and predicting method based on artificial intelligence

Abstract

The invention discloses a park carbon emission monitoring and predicting method based on artificial intelligence. According to the method, multi-modal data alignment modeling is carried out to form a Markov decision process, and self-adaptive alignment is realized through a reward function integrated with physical constraints; constructing a physical knowledge-guided Shenchang differential equation prediction model, and transmitting priori knowledge of a carbon balance equation through knowledge distillation; a depth auto-encoder is adopted to carry out anomaly detection, and root causes are positioned by cooperating with three methods; cross-park collaboration is realized through differential privacy federal learning and meta-learning; multi-agent deep reinforcement learning is adopted to explore a green electricity sharing and off-peak energy consumption cooperation strategy. The technical problems that the multi-modal data alignment precision is low, the data sparse scene prediction accuracy is poor, a cross-park collaborative learning mechanism is lacked, the anomaly diagnosis capability is insufficient, and regional collaborative optimization is lacked are solved.

Description

Technical Field

[0001] This invention relates to the fields of artificial intelligence and carbon emission monitoring technology, specifically to an artificial intelligence-based method for monitoring and predicting carbon emissions in industrial parks. Background Technology

[0002] Industrial parks are a significant source of carbon emissions, and monitoring and forecasting their carbon emissions is crucial for achieving dual-carbon goals. Accurate carbon emission monitoring and forecasting can not only support the refined management of industrial parks but also provide data support for regional coordinated emission reduction.

[0003] In existing technologies, carbon emission monitoring and prediction in industrial parks mainly employ methods based on statistical models or simple neural networks. However, these methods have the following shortcomings: First, carbon emission monitoring in the park involves... Various types of sensors, such as sensors, energy meters, and vibration sensors, have significantly different sampling frequencies, making it difficult to accurately align multimodal data in the time dimension. Traditional dynamic time warping methods are computationally complex and cannot effectively incorporate the physical constraints of carbon emissions. They also lack alignment accuracy when processing multi-sampling rate data, thus affecting the accuracy of multimodal data fusion estimation.

[0004] Second, carbon emission prediction performs poorly in scenarios with sparse data. Existing purely data-driven methods lack consideration for the physical constraints of carbon balance and conservation laws. When training data is limited, the models are prone to producing predictions that violate physical laws, have poor extrapolation capabilities, and cannot meet the prediction needs of newly built industrial parks or sensor failure scenarios.

[0005] Third, most existing technologies model individual industrial parks independently, lacking collaborative learning mechanisms across parks. While different parks differ in industry type, energy structure, and climate conditions, they also share certain common characteristics. Isolated modeling methods cannot utilize cross-park data and knowledge, resulting in new parks requiring long periods of data accumulation to build effective predictive models, leading to a long cold start cycle.

[0006] Fourth, existing technologies lack the ability to intelligently diagnose carbon emission anomalies. Traditional methods can only identify anomalies but cannot automatically pinpoint the root cause, requiring manual investigation, resulting in long response times and failing to meet the needs of real-time monitoring and rapid response.

[0007] Fifth, existing technologies lack cross-park collaborative optimization strategies, making it impossible to achieve regional-level emission reduction collaboration and failing to fully leverage the potential of green electricity sharing and staggered energy use across multiple parks.

[0008] Therefore, there is an urgent need for a carbon emission monitoring and prediction method that can achieve accurate alignment of multimodal data, improve the prediction accuracy in sparse data scenarios, support cross-park collaborative learning and optimization, and have intelligent anomaly diagnosis capabilities, so as to improve prediction accuracy, enhance model generalization ability, and shorten cold start time, thereby better supporting the carbon emission reduction work of parks and regions. Summary of the Invention

[0009] To address the aforementioned technical problems, this invention provides an artificial intelligence-based method for monitoring and predicting carbon emissions in industrial parks, comprising the following steps: S1: Deep reinforcement learning-driven multimodal spatiotemporal alignment models the spatiotemporal alignment problem of multimodal sensor data as a Markov decision process, adopts a deep Q-network to learn the alignment strategy, and achieves adaptive alignment by designing a reward function that incorporates physical constraints of carbon emissions, thereby obtaining time-aligned multimodal time series data; S2: Physics knowledge-guided neural ordinary differential equation prediction modeling, constructing a physics teacher model and a neural student model, passing the prior knowledge of the carbon balance equation to the neural network through a knowledge distillation framework, and using a residual learning network to capture factors not modeled by the physics model to obtain carbon emission prediction values. S3: Deep learning-driven anomaly detection and root cause intelligent analysis. It uses a deep autoencoder to learn normal carbon emission patterns, determines anomalies based on reconstruction errors, and uses attention weight analysis, counterfactual inference, and causal graph tracing to collaboratively locate the root cause of anomalies and generate a diagnostic report. S4: Differential privacy federated learning for cross-campus collaborative prediction. Each campus trains a deep neural network prediction model locally, clips the gradients and adds Gaussian noise before uploading them to the central server. The global model is then aggregated and updated through a federated averaging algorithm to achieve cross-campus collaborative learning. S5: Meta-learning-driven cross-park rapid transfer, constructing a task embedding network to encode park features, using an attention mechanism to learn task similarity, and performing meta-training on multiple source park tasks based on a model-agnostic meta-learning algorithm, enabling new parks to quickly adapt from the meta-model; S6: Collaborative optimization using multi-agent deep reinforcement learning. Each park is modeled as an agent, and an Actor-Critic architecture is used to learn emission reduction strategies. The reward function integrates carbon emission rewards, cost rewards, and constraint penalties. Through centralized training and decentralized execution, collaborative strategies for green electricity sharing and peak-shifting energy use are explored to optimize the total carbon emissions in the region.

[0010] Furthermore, the deep Q-network in S1 models the state-action value function and uses experience replay and target network mechanisms to stabilize the training process, enabling the alignment agent to adaptively adjust the alignment strategy according to the dynamic characteristics of carbon emissions.

[0011] Furthermore, S1 adopts a hierarchical reinforcement learning architecture. The high-level policy network determines the stationary period or the changing period based on the local variance of the energy consumption sequence, while the low-level policy network selects matching, insertion, deletion or skipping operations through a deep Q network, thereby improving computational efficiency while ensuring alignment accuracy.

[0012] Furthermore, the physics teacher model in S2 constructs a causal chain from energy consumption to carbon emissions based on the carbon balance equation, and the neural student model uses a neural ordinary differential equation framework to model continuous-time dynamics. The loss function includes distillation loss, true label loss, and physical consistency loss.

[0013] Furthermore, S2 introduces an attention mechanism to learn the contribution weights of three key components in the causal chain—heat load power, boiler thermal efficiency, and combustion completeness—to carbon emissions, thereby achieving an organic combination of data-driven approaches and physical knowledge.

[0014] Furthermore, the root cause analysis in S3 integrates the scores of three methods—attention weight analysis, counterfactual inference, and causal graph tracing—to generate a diagnostic report that includes anomaly description, root cause localization, and treatment recommendations.

[0015] Furthermore, the personalized federated learning in S4 divides the model into a global shared layer and a park-specific layer. The shared layer learns the general features across parks, while the specific layer adapts to the personalized features of each park.

[0016] Furthermore, the hierarchical migration strategy in S5 enables shallow feature extractors to be shared across parks, mid-level encoders to be shared among similar industry types, and deep predictors to be customized for specific parks.

[0017] Beneficial effects: 1. Improved Multimodal Data Alignment Accuracy: This invention models multimodal data alignment as a Markov decision process. By incorporating carbon concentration change rate constraints and causal temporal constraints into the reward function, the alignment results conform to the physical laws of carbon emissions. The hierarchical reinforcement learning architecture adaptively switches alignment modes according to data characteristics, which, compared with traditional dynamic time warping algorithms, ensures both alignment accuracy and computational efficiency.

[0018] 2. Improving Prediction Accuracy in Data-Sparse Scenarios: This invention constructs a neural network prediction model based on physical knowledge. Prior knowledge of the carbon balance equation is passed to the neural network through knowledge distillation, and a residual learning network compensates for complex factors not modeled in the physical model. This method maintains physical consistency even in data-sparse scenarios, avoiding the overfitting problem of purely data-driven models.

[0019] 3. Achieving Cross-Park Collaborative Learning and Rapid Transfer: This invention employs differential privacy federated learning and meta-learning to achieve cross-park collaboration. Each park collaboratively trains the global model without sharing original data, and the personalized federated learning strategy balances universality and adaptability. The meta-learning method enables new parks to quickly build predictive models with only a small amount of historical data, significantly shortening the cold start period.

[0020] 4. Achieving Automatic Anomaly Diagnosis and Root Cause Localization: This invention utilizes three methods—attention weight analysis, counterfactual inference, and causal graph tracing—to locate the root cause of anomalies, tracing the fault link backward along the causal chain from energy consumption to carbon emissions. The generated diagnostic report includes anomaly description, root cause localization, and remedial recommendations, achieving automatic anomaly diagnosis and shortening response time.

[0021] 5. Achieving Regional Collaborative Emission Reduction Optimization: This invention employs multi-agent deep reinforcement learning to achieve cross-park collaborative emission reduction optimization. The global reward term in the reward function encourages collaborative decision-making among the parks. Through centralized training and decentralized execution, each agent explores collaborative strategies for green electricity sharing and peak-shifting energy use, optimizing the total regional carbon emissions while meeting the production constraints of each park. Attached Figure Description

[0022] Figure 1 A flowchart illustrating the steps of the method described in this invention is shown. Detailed Implementation

[0023] Exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0024] Combination Figure 1 This invention provides an artificial intelligence-based method for monitoring and predicting carbon emissions in industrial parks, with the following steps: S1: Deep reinforcement learning-driven multimodal spatiotemporal alignment The industrial park's carbon emission monitoring system is equipped with a variety of sensors. The sensor directly measures carbon dioxide concentration at a sampling frequency of 1 Hz; the energy meter monitors the energy consumption of boilers and generators at a sampling frequency of 0.1 Hz; and the vibration sensor monitors the operating status of equipment at a sampling frequency of up to 100 Hz. The difference in sampling frequencies poses a challenge to the time alignment of multimodal data. Direct interpolation at fixed time intervals will lose transient information, while simple nearest neighbor matching will introduce significant time errors.

[0025] Carbon emission processes are subject to significant physical constraints. Carbon emissions from boiler combustion follow chemical reaction rates, and the rate of change in carbon concentration is limited by spatial diffusion and ventilation. Traditional dynamic time warping algorithms fail to reflect these physical constraints, potentially leading to alignment results that violate physical laws. This step models the spatiotemporal alignment of multimodal data as a Markov decision process, employs a deep Q-network to learn the alignment strategy, and incorporates the physical constraints of carbon emissions through a reward function.

[0026] The state space of a Markov decision process is defined as a triple. The current alignment position indicates that the energy consumption sequence is being processed. The data point and the first carbon concentration sequence Aligning data points, denoted as Historical path characteristics Record the past The alignment path is step-by-step, encoding the path's twist and smoothness using a one-dimensional convolutional network. (Cumulative error) This reflects the overall quality of the current alignment scheme. The complete state is represented as... .

[0027] The action space includes four alignment operations. Matching operation. Update the alignment position to This assumes that the two data points correspond in time. Insertion operation. Update the alignment position to This indicates that dummy points need to be inserted into the energy consumption sequence. Deletion operation. Update the alignment position to This indicates that the carbon concentration sequence skips the current point. (Jump operation) Update the alignment position to The jump step length Adaptively adjust based on the current mode: Set during stable periods. Used for rapid advancement, set during periods of change. It is designed to balance efficiency and accuracy.

[0028] The reward function is the core of the alignment agent's learning process. The reward function designed in this invention comprises three parts: alignment accuracy reward, physical constraint reward, and efficiency reward.

[0029] in: To reward alignment accuracy, alignment of data points with similar features and close timeframes is encouraged; Rewards are based on physical constraints, while penalties are imposed for alignments that violate the physical laws governing carbon emissions. To reward efficiency, shorter alignment paths are encouraged; the weighting coefficients are as follows: .

[0030] Alignment accuracy rewards take into account both feature similarity and temporal consistency.

[0031] in: The feature distance; The distance is the timestamp; the weighting coefficient is... and The feature distance is calculated using normalized weighted Euclidean distance:

[0032] in: For position The carbon concentration value, For position The carbon concentration value, in ppm; For position Energy consumption value, For position Energy consumption value, in kW; For position The vibration amplitude value, For position The vibration amplitude value, in mm / s; , , These are the standard deviations of each feature, used to eliminate the influence of dimensions; To prevent division by zero by small constants; the feature weights are respectively Carbon concentration, as a direct monitoring target, is given the highest weight.

[0033] Timestamp distance normalized to the zero-to-one interval:

[0034] in: Position of energy consumption sequence The timestamp, in seconds; carbon concentration sequence position timestamp; and This represents the boundaries of the timestamp range.

[0035] The key innovation of this invention is the physical constraint reward, which integrates the physical laws governing carbon emission processes into the reward design of reinforcement learning. The physical constraint reward includes constraints on the rate of change of carbon concentration and causal temporal constraints.

[0036] in: Rewards are based on the rate of change in carbon concentration. Rewards are based on causal and temporal constraints.

[0037] The carbon concentration change rate constraint is based on the physical limitations of the diffusion equation. In enclosed or semi-enclosed spaces, the change in carbon concentration is limited by both the diffusion coefficient and ventilation. The change rate is calculated by aligning adjacent carbon concentration data points and compared with a physically feasible maximum change rate threshold.

[0038] in: The time interval between adjacent carbon concentration data points after alignment, in seconds; The maximum physically feasible rate of change of carbon concentration is determined based on the park's ventilation conditions and emission source characteristics, in ppm / s, with a typical value of 5 ppm / s. is the penalty coefficient. This constraint ensures that the aligned carbon concentration sequence does not exhibit physically impossible abrupt changes.

[0039] The causal timing constraint ensures that energy consumption events precede or occur simultaneously with carbon emission events in time, consistent with the causal relationship of combustion chemical reactions:

[0040] in: For causal timing tolerance, taking into account measurement and transmission delays, it is set to 2 seconds; This is the penalty coefficient. This penalty is higher than the rate of change constraint because a violation of causality is a more serious physical error.

[0041] Efficiency rewards encourage alignment paths to be as short as possible, avoiding unnecessary insertion and deletion operations:

[0042] in: This is the path distortion penalty coefficient; This represents the penalty coefficient for jump operations. Matching operations do not increase path length and have a zero reward; insertion and deletion operations cause path distortion and incur a larger penalty; jump operations, while advancing quickly, may skip important features and incur a smaller penalty.

[0043] Deep Q-networks parameterize the state-action value function through neural networks. Network input is status. The feature representation concatenates alignment position, historical path features, and cumulative error into an input vector. The network structure consists of three fully connected layers with 128, 256, and 128 hidden neurons, respectively, using ReLU as the activation function. The network output is the Q-value corresponding to the four actions.

[0044] Training employs an experience replay mechanism and a target network mechanism to improve stability. The experience replay buffer stores the agent's historical interactions with the environment; each experience entry includes the current state, the action performed, the reward received, and the next state, denoted as a quadruple. The buffer capacity is set to 10,000 records. During training, gradient updates are performed by randomly sampling data from the buffer in batches of size 64, breaking the temporal correlation of the data. The parameters of the target network... Every 10 training epochs, the main network parameters are adjusted. Synchronize once to avoid drastic fluctuations in the target value.

[0045] The loss function is defined as the mean square error of the time-series difference error:

[0046] in: This serves as a buffer for experience replay. Discount factor; Indicates the buffer Calculate the expectation from the experience of random sampling.

[0047] To further improve alignment efficiency, this step employs a hierarchical reinforcement learning architecture. In practical applications, park operations exhibit distinct stable and variable periods. During stable periods, equipment operates at steady loads, with energy consumption and carbon emissions changing slowly; during variable periods, equipment starts and stops or loads are adjusted, causing rapid fluctuations in energy consumption and carbon emissions. The high-level policy network determines whether the current period is stable or variable based on the local variance of the energy consumption sequence.

[0048] The formula for calculating local variance is:

[0049] in: This refers to the size of a local window. The first in the energy consumption sequence The value of each data point; The average energy consumption within the window is calculated using the following formula: When position Near the sequence boundary, i.e. or When the window is truncated to the actual range of the sequence, the number of summation terms is adjusted accordingly.

[0050] when When the time is determined to be in a stable period, a coarse-grained alignment mode is adopted, allowing coarse-grained jump operations; when When a period of change is identified, a fine-grained alignment mode is used to limit the jump step size. Threshold Based on historical data from the park, a typical value is determined to be 10% of the global variance in energy consumption. Under the framework determined by the higher-level policy network, the lower-level policy network selects specific alignment actions through a deep Q-network.

[0051] The two networks are trained using an alternating optimization strategy. The high-level policy network aims to minimize the total alignment time and is optimized using the policy gradient method. The low-level policy network aims to maximize the cumulative reward and is optimized using the deep Q-learning method described above. The two networks share an experience replay buffer to achieve collaborative learning.

[0052] After this processing step, the multimodal sensor data achieves precise alignment in the time dimension. The output aligned sequence is denoted as... ,in The length of the aligned sequence. The feature vector at each time step. It includes three dimensions: carbon concentration, energy consumption, and vibration amplitude, in the form of The aligned data meets the requirements for predictive modeling in terms of both temporal consistency and physical constraint satisfaction.

[0053] S2: Physics-guided prediction modeling of ordinary differential equations After obtaining the aligned multimodal data, a carbon emission prediction model needs to be built. In practical applications, the challenge of data sparsity is often encountered. Newly built industrial parks have limited historical data accumulation during the initial operation phase, data is missing during certain periods due to sensor failure or maintenance, and data samples are scarce for certain operating conditions in seasonally operated parks. In these data sparse scenarios, purely data-driven deep learning models are prone to overfitting, and the prediction results may violate physical laws.

[0054] This step constructs a physics-guided neural ordinary differential equation prediction model. This model uses a knowledge distillation framework to transfer prior knowledge from a physics teacher model based on the carbon balance equation to a neural student model, ensuring that the neural network adheres to physical constraints while learning data patterns. Simultaneously, a residual learning network is introduced to capture complex factors that the physics model cannot model.

[0055] The physics teacher model constructs a causal chain from energy consumption to carbon emissions based on the carbon balance equation. This causal chain reflects three successive physical processes: energy consumption driving fuel consumption, fuel combustion causing chemical reactions, and chemical reactions producing carbon emissions. For a boiler system using natural gas as fuel, the relationship between energy consumption and fuel consumption rate is as follows:

[0056] in: For a moment The natural gas consumption rate, expressed in kg / s; This refers to the heat load power, measured in W. The boiler thermal efficiency is dimensionless. The lower heating value of natural gas is taken as [value]. J / kg.

[0057] Boiler thermal efficiency is not a constant value, but varies with the load rate. During partial load operation, thermal efficiency decreases due to increased heat loss. This invention uses a quadratic polynomial to fit the relationship between thermal efficiency and load rate:

[0058] in: The load factor is defined as follows: Dimensionless; Rated power of the boiler; fitting coefficients Determined based on the performance curves provided by the boiler manufacturer.

[0059] The main component of natural gas is methane. The chemical equation for combustion is: According to stoichiometry, the conversion factor from fuel mass to carbon emission mass is:

[0060] in: Here is the molar mass of carbon dioxide; This represents the molar mass of methane. This represents the mass fraction of methane in natural gas.

[0061] Actual combustion is not ideal complete combustion. Excess air coefficient, fuel mixture homogeneity, and burner design all affect combustion completeness. This invention introduces a combustion completeness coefficient. It is related to the air-fuel ratio and load factor:

[0062] in: This represents the maximum completeness of combustion. The excess air coefficient is dimensionless, with typical values ​​ranging from 1.1 to 1.3. The air-fuel ratio influence coefficient; The load factor influence coefficient; This represents the optimal load rate. The first term of the formula reflects that excess air can improve combustion completeness, while the second term reflects that excessive deviation from the optimal load rate will reduce combustion efficiency.

[0063] Based on the above physical processes, the relationship between carbon emission rate and heat load is as follows:

[0064] in: The cumulative carbon emission mass is expressed in kg. The physics teacher model calculates the predicted carbon emissions through numerical integration:

[0065] in: This represents the initial cumulative carbon emissions mass. The cumulative carbon emission mass predicted by the physical model is solved numerically using the fourth-order Runge-Kutta method.

[0066] While physics teacher models can provide predictions based on first principles, actual carbon emissions in industrial parks are influenced by many complex factors, including thermal efficiency decline due to equipment aging, the impact of ambient temperature on combustion air density and preheating losses, load fluctuations caused by operators' habits, and the mutual influence of multiple devices operating in parallel. These factors are difficult to accurately model using simple physical equations.

[0067] The neural student model employs a neural ordinary differential equation (NDE) framework, approximating the dynamic evolution of carbon emissions through a learnable neural network function. The NDE represents the continuous-time dynamics of carbon emissions as follows:

[0068] in: For a moment The hidden state vector; Input features include energy consumption, ambient temperature, and equipment status; For the dynamic function of the neural network parameterization, For network parameters; It is a time variable.

[0069] Neural Networks A fully connected network with four hidden layers was used, with 64, 128, 128, and 64 neurons in each layer, and tanh was used as the activation function. The network input was concatenated with the hidden states. Input features and time embedding The time embedding uses sine-cosine position coding:

[0070] in: For the first One frequency component; Number of frequency components; temporal embedding vector Temporal embedding enables the model to capture the periodic patterns of carbon emissions.

[0071] The solution to the constant differential equations is performed using the Dormand-Prince method (DOPRI5), which is a fifth-order adaptive step-size explicit Runge-Kutta method. From the initial hidden state... Departure, calculate any time Hidden state:

[0072] Carbon emission forecasts are decoded from the hidden state through a linear output layer:

[0073] in: To output the weight matrix; This is a bias term.

[0074] The knowledge distillation framework transfers knowledge from the physics teacher model to the neural student model. During training, the neural student model learns both the real labels and the soft labels from the physics teacher model. The loss function is designed as a weighted sum of three parts:

[0075] in: This represents the loss of the true label. This is due to distillation losses; The physical consistency loss is represented by the following weighting coefficients: .

[0076] The true label loss is calculated using mean squared error:

[0077] in: This represents the number of training samples; For a moment The actual carbon emission measurement value.

[0078] Distillation loss is expressed using the Kullback-Leibler divergence:

[0079] in: The mean is variance is The normal distribution; and The uncertainties predicted by the neural model and the physical model are respectively obtained by adding a variance prediction branch to the output layer.

[0080] Physical consistency loss ensures that the prediction results satisfy the carbon conservation law and nonnegativity constraints:

[0081] The carbon conservation loss requires that the predicted increase in carbon emissions equal the theoretical increase in carbon emissions corresponding to the input energy consumption:

[0082] in: The theoretical carbon emission increment based on energy consumption is calculated using a physics teacher model.

[0083] Non-negative loss penalty negative value prediction:

[0084] To further improve model performance, a residual learning network is introduced. Captures complexities not modeled in the physical model. Residual networks utilize aligned multimodal features. Input, output correction term The residual network adopts a residual block stacking structure, containing three residual blocks. Each residual block contains two layers of fully connected networks plus skip connections.

[0085] The final carbon emission prediction is a weighted combination of the physical model output, the output of the neural ordinary differential equation, and the residual correction:

[0086] Where: the fusion weights are respectively ,satisfy The physical model provides the basic constraints, the neural network uses frequent differential equations to learn the main dynamic laws, and the residual network compensates for detailed deviations.

[0087] Furthermore, an attention mechanism is introduced to learn the contribution weights of each link in the causal chain from energy consumption to carbon emissions to the final carbon emissions. The input to the attention mechanism is the feature representation of three key links in the causal chain, corresponding to heat load and power, respectively. Boiler thermal efficiency Complete combustion Three physical variables. Attention weights are calculated as follows:

[0088] in: For the first Attention score for each stage; For the first Feature vectors of each stage; , , These are learnable parameters.

[0089] The weighted causal chain characteristics are:

[0090] This feature serves as additional input to the regular differential equation, enhancing the model's ability to model causal relationships. Visualizing the attention weights can reveal the differences in the contributions of various physical components under different operating conditions. For example, during low-load operation, thermal efficiency fluctuations may become the dominant factor. The weighting is relatively high; under high load operation, combustion completeness may become a key factor, at which point... It has a high weight.

[0091] After this step, based on the aligned multimodal data... To obtain carbon emission forecasts for future periods ,in For the time domain of prediction, the prediction model maintains physical consistency in sparse data scenarios, while improving prediction accuracy through data-driven residual learning.

[0092] S3: Deep Learning-Driven Anomaly Detection and Root Cause Analysis After obtaining the predicted carbon emissions, it is necessary to perform anomaly detection on the actual monitoring data to promptly identify equipment malfunctions, sensor failures, or abnormal emission events. Traditional methods can only identify anomalies but cannot automatically pinpoint the root cause, requiring manual investigation and resulting in long response times. This step constructs a deep learning-based anomaly detection and root cause analysis model to achieve real-time identification and intelligent diagnosis of carbon emission anomalies.

[0093] Deep autoencoders are used to learn normal carbon emission patterns. An autoencoder consists of an encoder and a decoder. The encoder compresses high-dimensional carbon emission features into a low-dimensional latent space, and the decoder reconstructs the original data from the latent space. The encoder uses a three-layer fully connected network with 100, 50, and 20 neurons respectively; the decoder uses a symmetrical structure with 20, 50, and 100 neurons respectively.

[0094] The encoder output is a latent representation The calculation process is as follows:

[0095] in: For a moment The carbon emission characteristic vector, The feature dimension corresponds to three monitoring variables: carbon concentration, energy consumption, and vibration amplitude. , , This is the weight matrix; , , It is the bias vector; , , All are ReLU activation functions; This represents all parameters of the encoder.

[0096] The decoder reconstructs the original data from the latent representation:

[0097] in: , , This is the weight matrix; , , It is the bias vector; , It is the ReLU activation function; This represents all parameters of the decoder.

[0098] Training uses historical data from normal operating conditions, and the loss function is the reconstruction error.

[0099] in: This represents the number of normal samples. After training, the autoencoder can accurately reconstruct normal data, but its reconstruction error for abnormal data is relatively large.

[0100] Anomaly detection is determined by reconstructing an error threshold. For new monitoring data... Calculate its reconstruction error:

[0101] when When it is judged as abnormal, the threshold is used. The error is determined based on the reconstruction error distribution of normal data, and is typically set to the mean plus three standard deviations. To improve detection sensitivity, a time window for cumulative error is introduced.

[0102] in: The size of the time window. If the condition is determined to be persistent, the root cause analysis module is initiated. The threshold for persistent abnormality is... Set to 5 times the single-point anomaly threshold, i.e. .

[0103] Root cause analysis employs three methods—attention weight analysis, counterfactual inference, and causal graph tracing—to collaboratively locate the root causes of anomalies. Attention weight analysis, based on the attention mechanism of a variational autoencoder, identifies anomalous features that the model focuses on. The variational autoencoder introduces an attention layer between the encoder and decoder.

[0104] in: This is the attention weight vector, corresponding to the input features. One dimension; To query the weight matrix; the softmax function ensures that the attention weights are normalized, satisfying... .

[0105] Attention weights when an anomaly occurs The feature dimensions corresponding to elements with larger mid-range values ​​are those considered anomalous features by the model. By ranking the attention weights, the top-ranked features are extracted. The most prominent anomalous feature. The attention score is defined as:

[0106] in: For the first Attention-normalized scores of candidate root cause features; This represents the number of candidate root cause features extracted.

[0107] Counterfactual inference identifies root causes by observing whether anomalies disappear after removing a single feature. For candidate root cause features... Constructing counterfactual samples , of which The dimensional feature is replaced with the mean of the historical normal values:

[0108] in: Features The average value under normal operating conditions is calculated using historical normal data.

[0109] The counterfactual samples are input into the autoencoder for reconstruction, and the reconstruction error is calculated. ,in .like This indicates the removal of features. If the abnormality disappears, then the characteristic... As the root cause. Counterfactual inference is performed on all candidate features, and the root cause score is defined as:

[0110] in: Representation of features The degree of contribution to the anomaly; To prevent small constants from being divided by zero.

[0111] Cause-effect graphs trace the causal chain from energy consumption to emissions backwards to pinpoint the faulty link. A cause-effect graph includes four nodes: energy consumption... Thermal efficiency Complete combustion Carbon emissions and three directed edges: , , When abnormal carbon emissions are detected, each link in the chain is examined in reverse.

[0112] Define the state deviation of each stage:

[0113] in: These are reference values ​​for energy consumption, thermal efficiency, combustion completeness, and carbon emissions, respectively, determined by historical averages under normal operating conditions.

[0114] The score for cause-effect graph tracing is calculated using a normalized deviation:

[0115] in: For the first The normalized score of each causal chain node. The node with the higher score is the root cause node. If combustion completeness... The highest score indicates a burner malfunction or air-fuel ratio imbalance; if the thermal efficiency is... The highest score is likely due to boiler aging or increased heat loss; if energy consumption... The highest score is due to sudden load changes or equipment start-up / shutdown.

[0116] The final root cause score is calculated by combining the scores from the three methods:

[0117] Where: the weighting coefficients are respectively The sum of the three is 1. Based on the final scores, the root cause feature with the highest score is selected to generate a diagnostic report.

[0118] The diagnostic report consists of three parts: an abnormality description, root cause localization, and treatment recommendations. The abnormality description specifies the time when the abnormality occurred. Duration And the degree of abnormality, the degree of abnormality is defined as follows: Root cause localization provides root cause features and their scores. The proposed solution is to match similar faults based on a historical fault case database, using a nearest neighbor matching method to calculate the cosine similarity between the current abnormal feature vector and the historical fault feature vectors.

[0119] in: The first in the historical failure case library The feature vectors of each fault are used. The top 3 historical fault cases with the highest similarity are selected, and their corresponding handling measures are extracted as suggestions.

[0120] This step enables timely identification and automatic diagnosis of root causes for carbon emission anomalies. Combined with the prediction results from S2, this step determines whether the anomaly aligns with expected trends. If the prediction indicates an increase in carbon emissions, and an increase is actually detected, it is considered a normal fluctuation; if the prediction does not indicate an increase, but an increase is actually detected, it is considered an abnormal event, requiring root cause analysis. This synergistic mechanism of prediction and detection reduces the false alarm rate.

[0121] S4: Cross-Campus Collaborative Prediction Using Differential Privacy Federated Learning Building upon the predictive modeling of individual industrial parks, this step employs a differential privacy-preserving federated learning framework to achieve collaborative prediction across parks, further enhancing prediction accuracy and enabling rapid cold start for new parks. While different parks differ in industry type, energy structure, and climate conditions, they share commonalities in the fundamental patterns of carbon emissions. Through federated learning, each park collaboratively trains the global model without sharing raw data.

[0122] The basic process of federated learning includes four stages: local training, gradient uploading, global aggregation, and model distribution. Each campus independently trains a deep neural network prediction model on its local data. The local model has the same structure as the neural network differential equation model in S2, including an encoder, a neural network differential equation solver, and a decoder. Each campus performs... For the first round of local training, the loss function is:

[0123] Among them: superscript Number the park; For the park The number of training samples; These are the model's predicted values; These are actual measured values.

[0124] After local training is completed, each park calculates the amount of model parameter updates, i.e., the local model parameters. With global model parameters The difference:

[0125] To protect data privacy in each campus, gradients are pruned and Gaussian noise that meets differential privacy requirements is added before uploading. Gradient pruning limits the norm of each parameter update:

[0126] in: This is the clipping threshold; To prevent division by zero by small constants, this operation ensures that the gradient contribution of any single park is not excessive, preventing model inference attacks.

[0127] Add Gaussian noise after gradient clipping:

[0128] in: The mean is zero and the covariance matrix is Multidimensional Gaussian noise; The matrix is ​​the identity matrix. The noise standard deviation is calculated using differential privacy theory.

[0129] in: Budget for privacy and control the extent of privacy breaches; This represents the probability of privacy failure. The gradient with added noise is uploaded to the central server.

[0130] The central server uses a federated averaging algorithm to weight and aggregate noise gradients from multiple campuses. The aggregation weight is proportional to the number of samples from each campus.

[0131] in: This represents the number of campuses participating in federated learning. The global model parameters are updated as follows:

[0132] in: This represents the global learning rate. The updated global model parameters are broadcast to all campuses, and each campus downloads the global model and performs the next round of local training. Through multiple rounds of iterative training, the global model can integrate knowledge from multiple campuses.

[0133] To further improve the effectiveness of federated learning, a personalized federated learning strategy is adopted. The model is divided into a global shared layer and a campus-specific layer. The global shared layer includes an encoder and a neural network constant differential equation solver, which learns general features across campuses; the campus-specific layer includes a decoder, which adapts to the personalized features of each campus.

[0134] The training process for personalized federated learning is as follows: During local training, each campus updates parameters for both the globally shared layer and the campus-specific layer. When uploading to the central server, only the parameter updates for the globally shared layer are uploaded. Parameter updates for specific floors in the park Each park retains its own independent parameters. The central server only aggregates parameters from the globally shared layer.

[0135] The updated global shared layer parameters are broadcast to all parks, and each park updates the global shared layer parameters accordingly. At the same time, maintain the parameters of specific layers in the park. constant.

[0136] The loss function for the personalized strategy is designed as a weighted sum of the global loss and the personalized loss:

[0137] in: For the global shared layer in the park Data loss; For specific floors in the park Data loss; weighting coefficients are respectively .

[0138] The global loss calculation uses the output of the globally shared layer as an intermediate representation, and calculates the loss through a temporary decoder. The temporary decoder has the same structure as the campus-specific layer, but its parameters are initialized independently and remain fixed during global loss calculation; it is only used to evaluate the representation quality of the globally shared layer.

[0139] in: This is the output of the temporary decoder. Personalized loss calculations use the output of the full model:

[0140] This strategy ensures both generalization ability and adaptability, enabling the global model to learn common patterns of carbon emissions while adapting to the differences between various industrial parks.

[0141] After this step, each campus achieved collaborative learning through a differential privacy federated learning framework. The global model integrates knowledge from multiple campuses, improving the prediction accuracy of each campus. The differential privacy mechanism ensures that sensitive data from each campus is not leaked, and gradient pruning and noise addition prevent the original data from each campus from being inferred from the uploaded gradients even if the central server is attacked. Each campus obtains globally shared layer parameters that integrate knowledge from multiple parties, which can be used for subsequent transfer learning.

[0142] S5: Meta-learning-driven rapid cross-campus migration While federated learning can improve the prediction accuracy of individual parks, it still faces the challenge of a long cold start period for newly established parks or those with insufficient data accumulation. New parks require months or even years of historical data to build an effective predictive model during their initial operation. This step employs a meta-learning framework to achieve rapid migration prediction for new parks, enabling them to quickly build predictive models based on knowledge from similar source parks.

[0143] The core idea of ​​meta-learning is to learn how to learn quickly. By meta-training on multiple source tasks, the initial parameters of the model are optimized, enabling it to quickly adapt to new tasks. The meta-learning framework includes three key components: task embedding, similarity learning, and model-independent meta-learning.

[0144] The task embedding network encodes the park's multidimensional features into low-dimensional task embedding vectors. The park features include four categories: climate features, industry features, energy structure features, and emission features. Climate features include average annual temperature. Annual precipitation Sunshine hours Number of heating days Number of cooling days Five dimensions. Industry characteristics include the one-hot encoded vector of the dominant industry type. Output value scale Energy intensity The unique thermal encoding vector has a dimension of 5, corresponding to five industry types: chemical, steel, power, building materials, and light industry. The total dimension of industry characteristics is 7. Energy structure characteristics include the proportion of coal. Natural gas percentage Electricity share The proportion of renewable energy Four dimensions. Emission characteristics include total annual emissions. Emission intensity Peak emission period coding The peak emission periods are encoded as unique heat vectors with a dimension of 4, corresponding to the four seasons of spring, summer, autumn, and winter, resulting in a total dimension of 6 for the emission features. These features are then concatenated to form the park's feature vector:

[0145] The feature vector dimension is .

[0146] The task embedding network employs a three-layer fully connected network to encode the park feature vectors into low-dimensional task embedding vectors.

[0147] in: This is the weight matrix; , , It is the bias vector; It is the ReLU activation function; This represents all the parameters of the task embedded in the network; Embed the vector for the task.

[0148] Similarity learning employs an attention mechanism to learn the similarity between tasks. Given the task embeddings of the new campus... Task embedding of Heyuan Park The attention mechanism automatically focuses on feature dimensions important for similarity judgment. The attention score is calculated as follows:

[0149] in: , For the projection matrix of the query and key; This is the attention vector; This is the bias vector.

[0150] The similarity score is normalized to the zero-to-one range using the sigmoid function:

[0151] A higher similarity score indicates a greater similarity between the source and new campuses. Based on the similarity score, the source campus is selected from the previous ones. The most similar source campuses were selected as the basis for migration. The selection criteria were:

[0152] The Model-Independent Meta-Learning (MAML) algorithm performs meta-training on multiple source campus tasks to optimize the model's initial parameters. The goal of meta-training is to find a good initialization parameter. This allows for better performance on new tasks with only a small number of samples and gradient update steps, starting from this parameter. The meta-training process consists of an inner loop and an outer loop.

[0153] The inner loop simulates the rapid adaptation process to new tasks. For each source campus task... Initialize from metamodel In supporting sets Up Step gradient descent. The parameters for the step are updated as follows:

[0154] in: The learning rate for the inner loop; For the task The loss function is the mean squared error. After the update, the adapted model parameters are: .

[0155] Initialization parameters for the outer loop optimization metamodel. (In the query set) The performance of the adapted model is evaluated, and the meta-loss is calculated:

[0156] in: The number of tasks in the source park. Update the meta-model parameters using the meta-gradient:

[0157] in: This is the meta-learning rate. The calculation of the meta-gradient requires backpropagation through the gradient update chain in the inner loop:

[0158] in: The chain rule is used to calculate the gradient update steps in the inner loop, taking into account their impact.

[0159] Meta-training iteration In each round, tasks with a batch size of 16 are randomly sampled from the task pool of the source park for training. This yields the optimal meta-model initialization parameters. .

[0160] When a new campus requires modeling, a rapid adaptation process is executed. The new campus provides a small number of labeled samples. This typically includes 5 to 7 days of historical data. Initialization from the metamodel. On the support of the new park Step gradient descent:

[0161] Adapted model It can be used for carbon emission prediction in new industrial parks.

[0162] To further improve the transfer effect, a hierarchical transfer strategy is adopted. The model is divided into three parts: a shallow feature extractor, a mid-level encoder, and a deep predictor. The shallow feature extractor corresponds to the first two fully connected layers of the NCD model in S2, learning general time-series features, including trends, seasonality, and periodicity, which are shared across all industrial parks. The mid-level encoder corresponds to the third and fourth fully connected layers, learning industry-type-related features; chemical industrial parks, steel industrial parks, and power industrial parks each share their own mid-level encoders. The deep predictor corresponds to the NCD solver and output layer, learning park-specific mapping relationships and is customized for each industrial park.

[0163] The hierarchical migration execution process is as follows: For new industrial parks, the appropriate mid-level encoder is selected based on their industry type. The shallow feature extractor directly reuses the meta-model parameters. The mid-level encoder selects the corresponding pre-training parameters based on the industry type. The deep predictor is randomly initialized. .

[0164] On the new campus support set, only the deep predictor is fine-tuned, while the shallow feature extractor and mid-level encoder remain fixed. The fine-tuned loss function includes the prediction loss and the regularization loss:

[0165] in: To predict the loss, mean squared error is used; To regularize the loss and prevent deep predictors from deviating excessively from the meta-model; This is the regularization coefficient.

[0166] Predictive loss is defined as:

[0167] in: The number of samples supporting the new park.

[0168] Regularization loss is defined as:

[0169] in: These are the current parameters of the deep predictor for the new industrial park; These are the parameters for the deep predictor of the meta-model. The regularization loss encourages the predictor of the new park not to deviate excessively from the meta-model, maintaining consistency with the source park.

[0170] After this step, newly established industrial parks can quickly build predictive models based on knowledge from similar source parks. The cold start cycle is shortened from months to days, reducing the data requirements and time costs for modeling new parks. The hierarchical migration strategy enables the model to quickly adapt to the characteristics of different parks while maintaining cross-park generalization ability. The newly established parks obtain adapted predictive models that can be directly used for carbon emission prediction applications.

[0171] S6: Collaborative Optimization of Multi-Agent Deep Reinforcement Learning Building upon cross-park collaborative forecasting, this step employs multi-agent deep reinforcement learning to optimize cross-park collaborative emission reduction, aiming to further reduce total regional carbon emissions. Independent optimization of a single park is often limited by local resources and constraints, making it difficult to fully leverage the collaborative potential at the regional level. For example, a park might generate a large amount of green electricity during peak photovoltaic power generation but be unable to fully utilize it, while neighboring parks are still using fossil fuels. Through cross-park collaboration, parks with surplus green electricity can supply power to parks with insufficient green electricity, while simultaneously adjusting production scheduling across parks to achieve peak-shifting energy use, thereby reducing total regional carbon emissions.

[0172] Multi-agent deep reinforcement learning models each park as an agent, and these agents explore optimal cooperative strategies through collaborative learning. This step employs the Multi-agent Deep Deterministic Policy Gradient (MADDPG) algorithm, combined with a centralized training and decentralized execution paradigm. In the centralized training phase, each agent's value network receives global state and global action information, learning a value function that considers the behaviors of other agents. In the decentralized execution phase, each agent's policy network makes decisions based solely on local observations.

[0173] Each agent employs an Actor-Critic architecture, consisting of a policy network (Actor) and a value network (Critic). The policy network learns emission reduction strategies for the campus, taking the locally observed state as input and outputting control actions. The value network evaluates the value of the strategies, taking the global state and global actions as input and outputting a state-action value.

[0174] The state of an intelligent agent is defined as the operational state of the park and the state of the environment. local state Including current carbon emissions Energy consumption Production load rate Renewable energy power generation Energy storage system power Predicted future carbon emission trends Six dimensions. The state vector is represented as:

[0175] in: The carbon emission forecast for the next hour, as output by the S2 prediction model, is expressed in kg.

[0176] Global state For the concatenation of the local states of all agents:

[0177] in: The number of parks participating in collaborative optimization.

[0178] The actions of an intelligent agent are defined as control decisions made within the park. (Intelligent agent) action This includes production scheduling decisions and energy selection decisions. Production scheduling decisions include adjustments to production load. This indicates the adjustment percentage relative to the current load factor. Energy selection decisions include the proportion of coal used. Natural gas usage ratio Electricity usage ratio Satisfying the constraints and .

[0179] The action vector is represented as:

[0180] Global Actions For the concatenation of actions of all agents:

[0181] The reward function is designed as a weighted combination of carbon emission rewards, cost rewards, and constraint penalties. (Agent) The instant reward is:

[0182] in: Incentives for carbon emissions; As a cost incentive; To constrain penalties; the weighting coefficients are respectively .

[0183] Carbon emission incentives encourage reductions in carbon emissions. To encourage regional collaboration, carbon emission incentives include local and global incentives:

[0184] in: This is the global reward coefficient. When local carbon emissions decrease, i.e. The local reward is positive; the global reward is positive when the total regional emissions decrease. The global reward item allows each agent to consider the reduction of total regional emissions while optimizing local carbon emissions.

[0185] Cost incentives encourage lower energy costs and are defined as negative values ​​for energy costs.

[0186] in: , , The prices are the unit prices for coal, natural gas, and electricity, respectively, in yuan / kg, yuan / kg, and yuan / kWh. , The park The mass of coal and natural gas consumed within a time step, in kg; For the park The electrical energy consumed within a time step, expressed in kWh.

[0187] The relationship between the consumption of coal, natural gas, and electricity and their proportion of energy use and total energy consumption is as follows:

[0188] in: For the park Total energy consumption, expressed in J; , These are the calorific values ​​of coal and natural gas, respectively, in J / kg. This is the conversion factor from joules to kilowatt-hours, expressed in J / kWh.

[0189] Constraints and penalties ensure uninterrupted production and compliance with environmental comfort standards. Production constraints and penalties are defined as follows:

[0190] in: and For the park The permissible load factor range is determined by the production plan; Target load factor; This is the penalty coefficient. When the load factor exceeds the allowable range, a penalty is imposed, and the severity of the penalty is proportional to the degree of deviation.

[0191] Environmental comfort constraint penalty is defined as:

[0192] in: For the park At any moment The ambient temperature, in degrees Celsius; and For a comfortable temperature range, it is typically set between 18 and 26 degrees Celsius; This is the penalty coefficient.

[0193] The total constraint penalty is the sum of two parts:

[0194] The policy network Actor uses a three-layer fully connected network to store local state. This is mapped to the original action vector. The network outputs the original action. :

[0195] in: , , This is the weight matrix; , , It is the bias vector; It is the ReLU activation function; The hyperbolic tangent activation function restricts the output to... interval; This represents all the parameters of the policy network.

[0196] Since the value ranges of each dimension of the action are different, it is necessary to map the original network output to obtain the actual action to be executed. The mapping of load adjustment is as follows:

[0197] in: The first dimension of the original output of the policy network has a range of .

[0198] The mapping of energy usage proportions uses softmax normalization:

[0199] in: These are the last three dimensions of the original output of the policy network. Softmax normalization ensures that the sum of the three is 1 and that none of them are negative.

[0200] The Critic value network uses a four-layer fully connected network, with the global state as the input. and global actions The output is a state-action value. The network structure is as follows: state and actions Embedded through a single fully connected network layer:

[0201] in: , An embedding matrix for states and actions; , This is the bias vector.

[0202] The embedded vectors are concatenated and then input into a two-layer fully connected network:

[0203] in: , This is the weight matrix; , It is the bias vector; This represents all the parameters of the value network.

[0204] During the centralized training phase, each agent's value network receives global state and global action information. The training objective of the value network is to minimize the temporal difference error.

[0205] in: The target Q value; This serves as a buffer for experience replay. Indicates the buffer Calculate the expectation from the experience of random sampling.

[0206] The target Q value is calculated as follows:

[0207] in: and These represent the global state and global action at the next moment, respectively. Discount factor; For the target value network, its parameters Through soft update and main network parameters synchronous:

[0208] in: This is the soft update coefficient.

[0209] Global actions in the next moment Generated through the target policy network of each agent:

[0210] in: For intelligent agents The target policy network, its parameters Similarly, through soft updates and main network parameters synchronous.

[0211] The training objective of the policy network is to maximize the output of the value network. The loss function of the policy network is:

[0212] Among them: global actions The Middle The actions of each agent are generated by the current policy network. The actions of other agents are generated by their respective current policy networks.

[0213] The policy network updates its parameters through gradient ascent:

[0214] To improve exploration capabilities, Ornstein-Uhlenbeck noise is added to the output of the policy network during the training phase:

[0215] Among them: Ornstein-Uhlenbeck process Described by the following stochastic differential equation:

[0216] in: This is the noise vector; This represents the mean regression rate. Noise intensity; This is a Wiener process. The noise exhibits mean-regression characteristics, making it suitable for exploring continuous action spaces.

[0217] In the decentralized execution phase, the policy network of each agent is based solely on local observations. Make a decision. (Intelligent agent) The execution action is as follows:

[0218] The original action is converted into an actual action through the above mapping. This design ensures that each park has the ability to make independent decisions, and that each park can still execute control strategies independently even in the event of a communication outage.

[0219] Through collaborative learning among agents, multi-agent deep reinforcement learning can explore collaborative strategies for green electricity sharing and peak-shifting energy use. The green electricity sharing strategy is manifested as follows: when the renewable energy generation of a certain industrial park... When its needs exceed its own, the agent learns to reduce the proportion of local fossil fuel use. and Simultaneously, green electricity is transmitted to other industrial parks via the power grid. The parks receiving green electricity will correspondingly increase their electricity usage ratio. This will reduce the consumption of fossil fuels.

[0220] The mathematical description of green electricity sharing is: Park The remaining amount of green electricity is ,in For the park's own needs. When At that time, the park It can supply green electricity to the power grid. (Park) Green electricity can be purchased from the grid, and the amount purchased is recorded as follows: The constraints are satisfied:

[0221] The peak-shifting energy strategy is manifested in the agent learning to reduce production load during peak grid load and peak carbon emission periods. By adjusting Achieve this by increasing production load during off-peak periods through adjustments. This scheduling strategy smooths the regional electricity demand curve and carbon emission curve, reducing peak carbon emissions.

[0222] Peak-shifting energy use timing identification is based on carbon price signals. Defining the time frame. carbon price Carbon prices are high during peak seasons and low during off-peak seasons. Intelligent agents predict future carbon emission trends through learning. In relation to load adjustment, the agent automatically achieves peak-shifting energy use. When forecasts indicate that future carbon emissions will rise, the agent tends to reduce the production load rate; when forecasts indicate that future carbon emissions will be low, the agent can increase the production load rate.

[0223] To further enhance collaboration, each agent implicitly perceives the state of other zones through the global state input of the value network. During the centralized training phase, the value network receives the global state. This includes information on renewable energy generation, carbon emissions, and load factors for all parks. In this way, the agent can understand the state and collaborative potential of other parks during policy learning. For example, when the agent... When a value network learns that other industrial parks have surplus green electricity, it tends to increase its own electricity usage ratio. .

[0224] The training process employs an experience replay mechanism. The experience replay buffer stores historical experiences of multi-agent interactions. Each experience includes the global state, actions of each agent, rewards for each agent, and the global state at the next time step, denoted as a quintuple. The buffer capacity is set to 50,000 records. During training, gradient updates are performed by randomly sampling empirical samples of size 128 from the buffer.

[0225] Training iteration Each round consists of two phases: environment interaction and network update. In the environment interaction phase, each agent interacts with the network and the environment according to its current policy. In the first step, the interaction experience is stored in a buffer. During the network update phase, each agent... Sub-gradient updates are performed to update the value network and policy network separately.

[0226] Through this step, a cross-park collaborative optimization mechanism was established using multi-agent deep reinforcement learning. Each agent explored collaborative strategies for green energy sharing and peak-shifting energy use, achieving regional-level emission reduction optimization. Centralized training enabled each agent to consider global information during strategy learning, while decentralized execution ensured the autonomous decision-making capabilities of each park.

[0227] In summary, this invention addresses the technical problems of low multimodal data alignment accuracy, poor prediction accuracy in sparse data scenarios, lack of cross-park collaborative learning mechanisms, insufficient anomaly diagnosis capabilities, and lack of regional collaborative optimization through the coordinated operation of six steps. Each step supports the others: the alignment results of S1 provide the data foundation for S2; the prediction results of S2 provide S3 with anomaly identification and a decision-making basis for S6; the diagnostic information of S3 is fed back to S6; and the global model parameters of S4 are used by S5 for migration. These six steps form a complete carbon emission monitoring, prediction, and optimization system.

[0228] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for monitoring and predicting carbon emissions in industrial parks based on artificial intelligence, characterized in that, Includes the following steps: S1: Deep reinforcement learning-driven multimodal spatiotemporal alignment. The spatiotemporal alignment problem of multimodal sensor data is modeled as a Markov decision process. A deep Q-network is used to learn the alignment strategy. The reward function includes alignment accuracy reward, physical constraint reward and efficiency reward. The physical constraint reward includes carbon concentration change rate constraint and causal time series constraint. The time-aligned multimodal time series data is obtained. S2: Physics knowledge-guided neural ordinary differential equation prediction modeling. A physics teacher model based on the carbon balance equation and a neural student model based on the neural ordinary differential equation are constructed. The prior knowledge of the physics teacher model is transferred to the neural student model through a knowledge distillation framework. The loss function includes the true label loss, distillation loss and physics consistency loss. A residual learning network is introduced to capture factors not modeled by the physics model and obtain carbon emission prediction values. S3: Deep learning-driven anomaly detection and root cause intelligent analysis. It uses a deep autoencoder to learn normal carbon emission patterns, determines anomalies based on reconstruction errors, and uses attention weight analysis, counterfactual inference, and causal graph tracing to collaboratively locate the root cause of anomalies, generating a diagnostic report that includes anomaly description, root cause location, and treatment suggestions. S4: Differential privacy federated learning for cross-campus collaborative prediction. Each campus trains a deep neural network prediction model locally, prunes the parameter update amount and adds Gaussian noise before uploading it to the central server. The global model is aggregated and updated through a federated averaging algorithm. The model is divided into a global shared layer and a campus-specific layer to achieve cross-campus collaborative learning. S5: Meta-learning-driven rapid cross-park transfer, constructing a task embedding network to encode park features, using an attention mechanism to learn task similarity, and performing meta-training on multiple source park tasks based on a model-independent meta-learning algorithm to optimize the initial parameters of the model, enabling new parks to quickly adapt from the meta-model; S6: Collaborative optimization using multi-agent deep reinforcement learning. Each park is modeled as an agent, and an Actor-Critic architecture is used to learn emission reduction strategies. The reward function includes carbon emission rewards, cost rewards, and constraint penalties. The carbon emission rewards include local rewards and global rewards. Through centralized training and decentralized execution, collaborative strategies for green electricity sharing and peak-shifting energy use are explored to optimize the total carbon emissions in the region.

2. The method according to claim 1, characterized in that, The physical constraint reward in S1 includes carbon concentration change rate constraint and causal time sequence constraint. The carbon concentration change rate constraint calculates the change rate by aligning adjacent carbon concentration data points and compares it with the physically feasible maximum change rate threshold. When the threshold is exceeded, a penalty is imposed. The causal time sequence constraint ensures that energy consumption events precede or coincide with carbon emission events in time. When the causal relationship is violated, a penalty is imposed.

3. The method according to claim 1, characterized in that, The S1 adopts a hierarchical reinforcement learning architecture. The high-level policy network determines whether the current period is a stable period or a changing period based on the local variance of the energy consumption sequence. The low-level policy network selects specific alignment actions through a deep Q-network under the mode determined by the high-level policy network.

4. The method according to claim 1, characterized in that, The physical teacher model in S2 constructs a causal chain from energy consumption to carbon emissions based on the carbon balance equation, including the relationship between energy consumption and fuel consumption rate, the relationship between boiler thermal efficiency and load rate, the conversion relationship between fuel quality and carbon emission quality, and the relationship between combustion completeness, air-fuel ratio and load rate. The predicted carbon emission value is calculated through numerical integration.

5. The method according to claim 1, characterized in that, The physical consistency loss in S2 includes carbon conservation loss and non-negativity loss. Carbon conservation loss requires that the predicted carbon emission increment equals the theoretical carbon emission increment corresponding to the input energy consumption. Non-negativity loss penalizes negative predictions.

6. The method according to claim 1, characterized in that, The S2 introduces an attention mechanism to learn the contribution weights of three key components in the causal chain—heat load power, boiler thermal efficiency, and combustion completeness—to carbon emissions. The final carbon emission prediction value is a weighted combination of the physical model output, the output of the neural ordinary differential equation, and the residual correction.

7. The method according to claim 1, characterized in that, The S3 method combines root cause analysis with attention weight analysis, counterfactual inference, and causal graph tracing. Attention weight analysis identifies abnormal features based on the attention mechanism of a variational autoencoder. Counterfactual inference determines the root cause by replacing candidate root cause features with historical normal values ​​and calculating the reconstruction error change. Causal graph tracing checks the deviation of each link in the causal chain of energy consumption, thermal efficiency, combustion completeness, and carbon emissions in reverse.

8. The method according to claim 1, characterized in that, The differential privacy protection in S4 is achieved by pruning the parameter update amount and adding Gaussian noise. The pruning operation limits the norm of each parameter update, and the standard deviation of the added Gaussian noise is calculated based on the privacy budget and the privacy failure probability.

9. The method according to claim 1, characterized in that, The model-independent meta-learning algorithm in S5 includes an inner loop and an outer loop. The inner loop performs gradient descent to simulate a fast adaptation process on the support set of the source campus task, while the outer loop evaluates the performance of the adapted model on the query set and updates the initial parameters of the meta-model.

10. The method according to claim 1, characterized in that, The hierarchical migration strategy in S5 enables shallow feature extractors to be shared across parks, mid-level encoders to be shared among similar industry types, and deep predictors to be customized for specific parks.

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