104results about How to "Reduce forecast error" patented technology

The invention relates to a load prediction method TSR-MAEN based on decomposition representation and an attention encoder network. Firstly, the algorithm provides a trend-seasonal decomposition representation method based on comparative learning so as to enhance the decoupling performance of a model on a trend component and a seasonal component of a load sequence and the representation capability of decomposition characteristics. Secondly, in order to deeply mine and decompose time sequence information contained in a feature hidden space and dependency between features, the algorithm proposes a mixed attention encoder network; through segmented embedding, a trend-season dynamic projection mixed self-attention mechanism and a multi-head mixed output mechanism, trend and season multi-dimensional features after load sequence decomposition are effectively utilized to express and enhance the long-range dependence information extraction capability of the model. Experiments show that the TSR-MAEN algorithm provided by the invention has excellent performance in an electrical load prediction task of oil and gas development, and the prediction error is reduced by 8.51% compared with that of an optimal baseline algorithm.

The invention discloses a line loss prediction method and system based on integrated DBN-BP, and belongs to the technical field of power system data analysis. The method comprises the steps that firstly, a plurality of parallel DBN sub-networks are constructed, all the sub-networks adopt different activation functions, unsupervised pre-training is carried out with N antenna loss historical data and corresponding weather data as input, and high-dimensional robust features are automatically extracted; and then, taking the output of each sub-network as a feature, inputting the feature into a BP integrated network for supervised training, and finally fusing to obtain a high-precision line loss prediction value. Through the architecture of "unsupervised feature extraction + supervised integrated decision", the problems of strong dependency on annotated data and weak feature extraction ability in the prior art are effectively overcome; meanwhile, forward prediction can be simplified into efficient matrix operation through the full-connection structure of the model, the requirement for real-time dispatching of the power grid is met, excellent generalization ability is achieved, and reliable data support is provided for economical and safe operation of the power grid.

The invention belongs to the technical field of spectral analysis, and discloses a method and a device for correcting a rotating matrix effect of a surface-enhanced laser-induced breakdown spectroscopy technology. The method comprises the following steps: 1, preparing a sample by a standard addition method; step 2, performing Youden calibration and sample preparation; step 3, performing SENLIBS sample preparation and spectrum acquisition; respectively carrying out SENLIBS sample preparation and spectrum collection on the sample solutions obtained in the step 1 and the step 2 to obtain corresponding absolute spectrum intensities of the two layers to be analyzed; step 4, establishing a calibration curve of the SENLIBS technology assisted by the standard addition method; step 5, obtaining a constant error through Youden calibration; step 6, constant error correction is carried out; and calculating the element concentration in the corrected solution to be detected. According to the invention, the constant error introduced by converting the carrier from a liquid phase to a metal carrier can be corrected, and the quantitative analysis accuracy of the SENLIBS technology is improved.

The invention discloses a vehicle network interaction flow optimization method based on information and physical fusion, and belongs to the technical field of intelligent traffic and energy internet crossing. Comprising the steps that a vehicle network is divided into a plurality of grid areas, and each grid area comprises physical layer operation data and information layer operation data; constructing a vehicle network diagram structure, and calculating the schedulable potential of the EV in the stay duration of the charging station; according to the schedulable potential and the information layer operation data of the EV in the stay time of the charging station, the method aims at minimizing the peak-valley difference, maximizing the renewable energy consumption, the vehicle network interaction income and the operation income. Constructing an objective function of the multi-objective flow optimization model, an EV charging and discharging power constraint, an EV battery charge state constraint, a charging pile capacity constraint and a power grid safety constraint; and solving the multi-target traffic optimization model to obtain a vehicle network interaction traffic optimization result. According to the method, the prediction error of the EV arrival time is reduced, and the V2G flow regulation and control are refined.

The application provides a three-dimensional anisotropic composite material heat conductivity coefficient prediction method and system, and relates to the technical field of heat analysis, and comprises the following steps: establishing a heat transfer unit cell model of a to-be-detected three-dimensional composite material in different directions based on structure parameters and material parameters of the to-be-detected three-dimensional composite material; the to-be-detected three-dimensional composite material is a three-dimensional orthogonal structure; the material parameters comprise first heat conductivity coefficients of each material; taking solid heat conduction as a current heat transfer form, determining equivalent thermal resistances corresponding to the heat transfer unit cell model of different regions by using an equivalent thermal resistance method and the first heat conductivity coefficients, establishing a heat transfer connection relationship between each equivalent thermal resistance by combining a Fourier equation and a thermal resistance network method, and determining first total thermal resistances along each heat transfer direction of the to-be-detected three-dimensional composite material; the heat transfer connection relationship comprises series connection and parallel connection; converting the first total thermal resistances based on overall size parameters of the heat transfer unit cell model, and determining an equivalent heat conductivity coefficient of a target direction.

The invention relates to a soil organic matter prediction method based on multi-feature fusion, and belongs to the technical field of soil organic matter prediction. The method comprises the following steps: preprocessing acquired soil data to obtain spectral features, and performing time domain reconstruction of frequency domain signals to obtain time domain data; obtaining an optimal delay time and an optimal embedding dimension based on the time domain data, and performing phase space reconstruction to obtain a phase space trajectory; chaos features are extracted based on the phase space trajectory, and the extracted chaos features, the optimal delay time and the optimal embedding dimension serve as final chaos features; obtaining a vegetation index based on the spectral feature and taking the vegetation index as an index feature; and inputting the spectral features, the final chaotic features and the index features into a constructed double-flow low-rank interaction network model to obtain a soil organic matter prediction result. The objective of the invention is to solve the technical problem of low prediction precision caused by the fact that spectral features extracted in the prior art cannot comprehensively represent complex nonlinear characteristics of soil.

The application provides a global optimization method of an air conditioning system and a storage medium, and relates to the technical field of intelligent control of air conditioning systems. The application first screens target variables through global sensitivity analysis and generates high-quality training data. Then, the grid search is used to optimize the hyperparameters and verify the generalization ability. Then, the multi-class global optimization algorithm is used to optimize the optimization variables and extract performance data. Based on multi-dimensional trade-off analysis, the target combination of the model and the algorithm is determined to achieve the collaborative optimization of the prediction accuracy, optimization accuracy and optimization speed. Finally, the target combination is embedded in the global optimization architecture and combined with the interference factor prediction value to output the set value, ensuring that the optimization scheme can dynamically adapt to environmental and load changes, significantly reducing the total energy consumption of the air conditioning system, while ensuring the system operation stability and indoor thermal comfort, and realizing the global optimal operation effect.

This invention belongs to the interdisciplinary field of artificial intelligence and geological resource assessment, specifically relating to a method and system for predicting the energy storage potential of salt caverns based on multi-source geological information. It aims to address the problems of low prediction accuracy and difficulty in balancing breadth and precision in existing technologies due to single-source data, static models, and lack of adaptive capabilities. The method includes: acquiring multi-source data such as seismic, well logging, core, and geostress data; constructing a geological feature fusion vector containing seven core parameters; establishing a high-dimensional nonlinear mapping model driven by a deep neural network; dynamically adjusting the prediction range based on data coverage density and confidence level; and achieving continuous model iteration through online incremental learning. The system integrates data quality assessment, anomaly handling, and distributed computing modules. By adopting the above technical solutions, this application can achieve a significant improvement in prediction accuracy and adaptive intelligent adjustment of the prediction range.

The application discloses an industrial robot predictive maintenance system based on machine learning, and relates to the technical field of industrial robot maintenance.The application comprises a data acquisition module, a preprocessing module, a feature engineering module, a hybrid prediction model unit, a meta-learning and field adaptation module, a federal learning coordination module, a digital twin sample generation module and a dynamic decision engine unit.The application has the following advantages: through the collaborative mechanism of the meta-learning and field adaptation module, a model-agnostic meta-learning algorithm is used to train a cross-brand general feature extractor;different brand data distributions are confused by combining an adversarial field adaptation network;the "data gap" caused by the definition of sensor parameters and the difference in sampling frequency in traditional modeling is broken through;the model is collaboratively trained under the premise that the data of each brand is not out of the local;the global model containing the commonness of cross-brand faults is generated;and the "data island" problem caused by commercial security requirements in the industrial scene is solved.

The application relates to the technical field of hydrogeological monitoring, in particular to a hydrogeological dynamic monitoring and analysis system and method based on big data; specifically, hydrogeological data is collected in real time, data denoising, space-time calibration and error correction are carried out in combination with edge computing, noise and drift error are eliminated by adopting variational mode decomposition, geological model constraint and wavelet transform, and the storage and transmission efficiency is optimized by increment encoding and Huffman encoding, and a quadratic form check code is generated to ensure data reliability; a space-time deep learning model and a self-attention mechanism are fused to predict a hydrological evolution trend, adaptive anomaly detection is realized by combining DBSCAN clustering and prediction error analysis; water resource allocation strategies are optimized through reinforcement learning, risk levels are evaluated in layers, and management schemes are generated, and finally, monitoring data, abnormal scores and decision results can be visually displayed. The application integrates multi-source data fusion, space-time modeling and intelligent decision-making, and improves hydrological monitoring precision and response efficiency.

This invention relates to the field of drainage flow data processing technology, specifically to an intelligent control method and system for drainage efficiency based on drainage flow monitoring data. The invention comprises a sampled data sequence and a predicted data sequence; it obtains multiple data segment groups from the sampled data sequence and the predicted data sequence based on the volatility of the data in the sampled data sequence; it obtains the prediction deviation of each data segment group based on the difference distribution characteristics and relative distance between the two data sequences in each data segment group; it obtains the relative prediction deviation difference at real time based on the difference between the prediction deviation of the data segment group at the real time and the prediction deviation of other data segment groups within the historical range; it obtains the weighted prediction data for the next time step; it further obtains the predicted gate opening degree; and it controls the drainage gate. This invention optimizes the gate opening degree and improves drainage efficiency by obtaining accurate drainage flow prediction data for the next time step.

The present application relates to the technical field of financial risk assessment, in particular to a kind of agricultural loan risk assessment analysis method based on big data, comprising the following steps: obtaining multi-dimensional associated data sequence of peasant household, analyzing node relationship and screening main path, according to main path logic vector judging path angle and screening collaborative fragment, extracting node coordinates to calculate spatial offset, sample path projection screening and marking, sequentially comparing path structure to identify time progression relationship, obtain agricultural loan risk assessment analysis data list.In the present application, by constructing the multi-dimensional data atlas of agricultural production cycle, the synergistic effect and spatial offset in the path are revealed, the risk assessment accuracy is improved, the evaluation can adapt to the dynamic changes of agricultural production, the prediction error caused by natural environment and periodic factors is reduced, the timeliness and accuracy are enhanced, the defects of traditional methods ignoring agricultural environment are effectively avoided, and the operability and decision support capability of loan risk assessment are improved.

This invention discloses a method and apparatus for predicting the planar shape of medium-thick plates based on neural networks, relating to the field of rolling technology. The method includes: acquiring finite element simulation data of medium-thick plate rolling to construct a sample set; training a neural network model, taking rolling process parameters as input, and outputting key point height data characterizing the head and tail shapes; combining the key point height data with boundary conditions where the edge height is zero, constructing and solving a system of linear equations of even-degree polynomials to generate a head and tail shape fitting prediction curve; and superimposing and integrating this prediction curve with the edge shape data of previous passes and the planar shape control pass data to generate the final planar shape prediction result for the medium-thick plate. This invention integrates simulation data and physical boundary constraints, effectively improving the stability of equation solving and the accuracy of shape prediction, and realizing the dynamic integration of deformation data from multiple passes.

The application discloses a high-stress soft rock tunnel large deformation prediction method based on deep learning, and belongs to the field of tunnel engineering geological disaster prediction. Through the construction of a double-branch network architecture of "geological pattern identification-deep learning prediction", the accurate prediction of tunnel large deformation is realized. First, six qualitative indexes such as rock mass structure, weathering degree and underground water condition and three quantitative indexes such as strength stress ratio, support stiffness and equivalent hole diameter are collected, and after coding and normalization preprocessing, they are respectively input into the upper and lower networks: the upper layer adopts a convolutional neural network to identify the surrounding rock geological pattern, and outputs the probability distribution of six types of large deformation modes such as soft homogeneous type and block structure type; the lower layer predicts the deformation amount and divides the risk grade through a fully connected neural network based on the geological pattern identification result and the quantitative index. The application can fully reflect the complex coupling mechanism of high-stress soft rock tunnel large deformation; and adaptive prediction is realized for different geological patterns.

This invention discloses a method for predicting gate switch oscillation in GaN-HEMT devices, belonging to the field of semiconductors and power devices. The invention trains a BPNN network model to obtain a preliminary mapping relationship between parasitic capacitance and gate-source voltage based on the BPNN algorithm model. Then, a Cascode-based two-port network function for GaN HEMT is established. The parasitic capacitance value of the GaN HEMT device under all S-parameters is obtained experimentally. This extracted parasitic capacitance value is then input into the BPNN network model as a dataset to obtain Vo. GS_m V under all S parameters DS C GS C GD The relationship between the surface and the curve is shown. Simulation results show that the standard error of the prediction method of the present invention is smaller than that of ANN and PSO, proving that the detection method for GaN-HEMT gate switch oscillation based on the BPNN algorithm model of the present invention has high prediction accuracy.

The invention discloses a wind measurement data correction method based on flow field simulation, and the method comprises the following steps: obtaining the current topographic data and historical target data of a target mountainous area; identifying key weather factors according to historical target data; constructing a quantitative relation model of the key weather factors and the surface form change; obtaining a topographic data prediction value of the target mountainous area; constructing a prediction digital elevation model according to the topographic data prediction value; obtaining corrected wind speed prediction data; comparing the corrected wind speed prediction data with the actually measured wind speed data of the field anemometer tower, and calculating the deviation; and constructing a space correction function based on the deviation, and applying the correction function to subsequent wind resource evaluation or fan layout optimization to realize dynamic correction of the wind measurement data. The fan flow field prediction method solves the problems that fan flow field prediction precision has a large error and the prediction result is not high in accuracy.

The application discloses a kind of based on iTransformer's heat load prediction method, belong to heat supply system heat load prediction technical field, including: collection historical meteorological data and corresponding heat load data, constructs heat load dataset;Construct network model based on iTransformer, and utilize heat load dataset to carry out learnable confrontation attack training, obtain heat load prediction model;Meteorological data of prediction area is input to heat load prediction model, and the corresponding heat load prediction result is output;The method of the application realizes heat load prediction by constructing network model based on iTransformer, eliminates the prediction error caused by label correlation, and improves the robustness of the proposed model by learnable confrontation attack training method.

This invention discloses a container ship trajectory prediction method based on EMD-PSO-GRU-RBFNN, belonging to the field of intelligent shipping technology. The method takes ship AIS data as input, including time, longitude, latitude, speed, and heading angle information; preprocesses the AIS data to divide it into training and test sets; constructs a trajectory prediction model, calculates the difference between the predicted values ​​of the training set and the actual ship position coordinates to obtain the residual signal; adaptively decomposes the residual signal using empirical mode decomposition to obtain intrinsic mode components and residual components; constructs prediction models for high-frequency and mid-to-low-frequency components, reconstructs the prediction results of each component to obtain the residual prediction value, and superimposes it with the baseline prediction results of the test set to obtain the corrected test set output; the corrected test set output is then evaluated. This invention can model and compensate for trajectory errors at different frequency scales, improving the stability and reliability of trajectory prediction.

A virtual power plant response effect evaluation method and system, historical data are collected and time sequence characteristic sequences and context characteristic vectors containing operation scene characteristics are constructed; the two are input into a context attention-based deep time sequence network CA-DTN model, an original baseline load curve is predicted, the original baseline is dynamically corrected based on actual load data of a response event day, an approved baseline is generated, the approved baseline is analyzed and judged to determine whether it belongs to an abnormal fluctuation day, when it is determined to be abnormal, the approved baseline is subjected to secondary dynamic checking, including model performance drift detection and baseline backtracking consistency analysis, and the CA-DTN model is optimized according to the checking result, and the final approved baseline is output; the actual load curve is compared with the final approved baseline, core indexes such as response amount and response completion rate are calculated, and a visual evaluation report is generated. The corresponding system includes function modules for implementing the above steps. The CA-DTN model significantly improves the prediction accuracy and market adaptability.

This invention discloses a method and apparatus for suppressing multiples in marine towed cable seismic data. The method first acquires multi-azimuth towed cable seismic data, performs three-dimensional consistency processing to eliminate differences in excitation, reception, and sampling; constructs a pseudo-three-dimensional water layer multiple model based on the processed data, extracting models from each azimuth; suppresses shallow water multiples in each azimuth data using a subtraction method, obtaining data after multiple suppression and the removed multiples data; merges and matches multiples removed from different azimuths, extracting effective signals; finally, merges the data after multiple suppression and the effective signals to output high-quality demultiplexed seismic data. This invention solves the problems of incomplete suppression and signal loss in conventional methods through multi-azimuth collaborative modeling and effective signal re-addition, improving the signal-to-noise ratio of seismic data and the reliability of geological interpretation, and is suitable for seismic exploration in shallow marine environments.

The application discloses a power prediction method and device, equipment and storage medium, and relates to the technical field of new energy. The method comprises the following steps: acquiring an original historical power time sequence, and decomposing the original historical power time sequence to obtain target power time sequences in a plurality of different frequency band ranges; an improved xLSTM model is used to output initial power prediction sequences of the plurality of different frequency bands according to the target power time sequences, wherein information fusion weights in the improved xLSTM model are determined according to fluctuations of power in the original historical power time sequence; and the initial power prediction sequences of the plurality of different frequency bands are mixed to obtain target power prediction. The technical scheme of the embodiment of the application has higher prediction accuracy and lower prediction error.

This invention relates to a regional generalization method for an adaptive graph spatiotemporal prediction model for wind power clusters, belonging to the field of wind power prediction and new energy power system dispatching technology. The technical solution is as follows: first, a regional generalization method for the adaptive graph spatiotemporal prediction model of wind power clusters is used to solve the adaptability and accuracy problems of cross-regional prediction; then, the transparency and credibility of prediction decisions are improved by relying on the model structure interpretability analysis method. This includes the following two steps: first, achieving effective generalization prediction; second, endowing the prediction with interpretability. The beneficial effects of this invention are: significantly improved generalization performance, reducing computational costs and deployment cycle; optimized prediction accuracy, enabling the adaptive graph spatiotemporal prediction model of wind power clusters to more accurately capture the spatiotemporal correlation between wind farms, reducing short-term wind power prediction errors by 10%-20%, and providing more reliable data support for grid dispatching; and enhanced interpretability, meeting the requirements of the dispatching system for model auditability and reducing operation and maintenance decision risks.

The invention discloses a financial time series data multi-scale feature analysis and prediction method and system, and belongs to the technical field of financial data processing and deep learning, and the method comprises the steps: obtaining and preprocessing multi-source financial time series data; performing multi-scale decomposition by adopting discrete wavelet transform and empirical mode decomposition to generate a trend component, a periodic component and a noise component; respectively extracting time dependence characteristics of each component through a time sequence Transform coding module; multi-scale features are subjected to adaptive weighted fusion through a multi-scale attention fusion module; outputting a predicted value and a confidence interval through a probability prediction module; and the closed-loop optimization module adjusts decomposition parameters and attention weights according to prediction error feedback, so that different frequency components of financial time series data can be effectively separated, a multilevel time dependency relationship is captured, and adaptive feature fusion and online optimization are realized.

This invention provides a method for predicting the dominant frequency of blasting vibration by integrating parameter fluctuation processing and the Dream Optimization Algorithm (DOA), belonging to the field of underground engineering safety control technology. The system includes a data acquisition and uncertainty modeling module, a data preprocessing and feature construction module, a hyperparameter optimization module, and a prediction model training and result output module. The method obtains relevant parameters through on-site investigation and monitoring, and generates an extended sample set using a combination of probabilistic perturbation modeling and fuzzy triangular modeling. The data is preprocessed and features are selected. The DOA algorithm is used to globally search the hyperparameters of the Support Vector Regression (SVR) model, and the optimal hyperparameter combination is selected by combining a robustness fitness function. The SVR model is trained, and prediction results and uncertainty prediction intervals are generated. This invention can explicitly characterize the uncertainty of geological parameters, achieve efficient global optimization of hyperparameters, improve prediction accuracy, robustness, and generalization ability, and is applicable to different geological conditions and blasting scenarios. It can be extended to various blasting dynamic response prediction tasks.

This invention belongs to the field of marine engineering antifouling material technology and artificial intelligence optimization technology. It discloses a collaborative optimization method for the antifouling stability and safety of antifouling materials used in debris-blocking nets, comprising the following steps: Step 1, collecting multi-dimensional data and preparing samples of the antifouling materials for the debris-blocking nets; Step 2, constructing and training an AI prediction model: selecting key feature parameters from the multi-dimensional data, using these key feature parameters as core feature vectors to construct a dual-objective prediction model, which outputs predicted values ​​for the material's antifouling stability index and environmental safety index; Step 3, collaborative optimization under multiple constraints: setting multiple constraints for the material; using the hybrid meta-heuristic algorithm ALO-KHO to solve the multi-objective optimization problem; selecting the optimal solution from the Pareto optimal solution to obtain the parameter combination of the antifouling materials for the debris-blocking nets with the highest score. The prediction model of this invention has high accuracy, strong generalization ability, and significant multi-objective collaborative optimization effect.

The invention relates to an integrated experimental IGBT device press fitting clamp and a use method, the clamp comprises a lower pressing plate, a first clamping pair, a second clamping pair, an upper pressing plate, an IGBT device and a pressure applying assembly, the lower pressing plate and the upper pressing plate are fixedly connected through a double-thread screw to form a rigid stress frame; a first clamping pair is fixedly arranged above the lower pressing plate; a pressure applying assembly is fixedly arranged on the upper pressing plate; a second clamping pair is arranged on the four double-thread screws in an up-down sliding mode. And a pressure sensor is fixedly arranged on the second clamping pair. The heating table and the pressure sensor are arranged in the press-fitting clamp, quantitative analysis in IGBT device process verification, limiting working conditions and failure analysis experiments is achieved, and errors of experimental result analysis are reduced. The trapezoidal conduction block uniformly diffuses the point clamping force of the ball stud to the surface of the device, so that uniform stress is ensured, and damage to the device caused by local stress concentration is avoided; and the spherical contact structure of the ball stud can automatically compensate the angle deviation.

The invention provides a method for generating a three-dimensional solid geologic model of a mine. The method comprises the following steps of: 1, acquiring and preprocessing multi-source mine geologic data; 2, constructing a preliminary mine three-dimensional entity geologic model: based on the multi-source mine geologic data preprocessed in the step 1, performing discretization processing on the geologic data by adopting a three-dimensional gridding method, generating a basic mine three-dimensional entity geologic model of the mine by using modeling software, identifying special structures of cracks and faults in the geologic data, and establishing a preliminary mine three-dimensional entity geologic model; carrying out special structure modeling to obtain a fault model; the fault model is adopted to refine the basic mine three-dimensional entity geologic model, and a preliminary mine three-dimensional entity geologic model is obtained; and step 3, carrying out iterative optimization and uncertainty quantification on the model precision. And 4, dynamically adjusting the model in real time. The model generated by the method has the characteristics of low data processing complexity, high model precision and comprehensive and definite geological information, and the performance of the mine three-dimensional entity geological model is integrally improved.

The invention discloses a flood classification and identification method based on flood feature analysis, and belongs to the technical field of hydrological forecasting and flood control and disaster reduction. According to the method, for the problem that a traditional hydrological model is unstable in performance when processing different types of floods, feature indexes of historical flood events are extracted through a system, advanced dimension reduction, clustering and recognition technologies are combined, accurate classification and real-time recognition of the flood events are achieved, and therefore differentiated parameter sets are provided for the hydrological model; and the accuracy and adaptability of flood forecasting are remarkably improved. The method not only reduces the influence of hydrological process heterogeneity on forecasting precision, but also provides more reliable technical support for flood control and disaster reduction decision-making. And taking M basin flood forecasting application as an example, the reasonability and effectiveness of the method are verified.

The invention provides a cross-game user LTV curve prediction method based on a dynamic quantile correction mechanism. The cross-game user LTV curve prediction method comprises the following steps: (1) obtaining basic training data and processing feature engineering; (2) quantile random forest basic model set training; (3) obtaining short-term alignment data of a target game and carrying out feature adaptation processing; (4) dynamically calculating an optimal quantile parameter; (5) predicting the long-term life cycle value of the new user; and (6) fitting a long-term life cycle value recovery curve. The core idea of the invention is as follows: a traditional point prediction model is transformed into a quantile prediction model, a dynamically optimized quantile parameter calculation module is introduced, and the module calculates a group of optimal quantile parameters by utilizing short-term data (such as 60 / 90 days) which is relatively easily obtained by a target game (or a new game), so that the optimal quantile parameters are obtained. The method is used for correcting the predictive output of the basic model for long-term targets (such as 180 days), thereby adapting to data drift and cross-game differences.

The application provides an atmospheric monitoring point distribution optimization identification method, device and equipment and a storage medium. It relates to the technical field of environmental monitoring. The method comprises the following steps: calculating Voronoi area and neighborhood density as node features based on the latitude and longitude of the site, combining with the AQI correlation to construct a graph structure; training a PollutionGNN model by using a double-layer GAT network with residual connection, establishing a three-objective optimization model of site number, coverage area and prediction error, and realizing the Pareto optimal solution set of monitoring efficiency by using an NSGA-III algorithm. The application solves the problem that the traditional point distribution method is difficult to balance the cost, coverage and precision, provides a theoretical basis for cross-regional application through the influence mechanism of geographical environment and pollution source structure on algorithm performance, finally realizes the upgrading of the monitoring network from experience layout to intelligent optimization, and provides an efficient and reusable solution for different cities.