148results about How to "Reduce forecast error" patented technology

The invention discloses a distributed photovoltaic short-time output dynamic prediction method and system. The method comprises the following steps: acquiring micro-meteorological data, a cloud picture image and historical output data of a target area; calculating a building shadow shielding coefficient, and generating shadow influence factor time sequence data; obtaining cloud image occlusion index time series data based on the cloud image and a pre-constructed cloud image occlusion prediction model; fusing the data, and constructing and training a short-time output dynamic prediction model based on a long short-term memory network; predicting photovoltaic output by using real-time data and the trained model, and adjusting the model according to a prediction error and a confidence interval; according to the method provided by the invention, various factors such as microclimate, building shadow and cloud layer shielding are comprehensively considered, and the complex nonlinear relationship between the factors and the photovoltaic output is captured by using the deep learning model, so that the accuracy and robustness of distributed photovoltaic short-time output prediction are improved.

The invention discloses a park comprehensive energy optimization scheduling method based on artificial intelligence, and relates to the technical field of energy scheduling, and the method comprises the steps: employing a Temporal Fusion Transform model to carry out the time series modeling of electric, thermal and cold loads and wind-solar power generation output in a park, capturing the load change trend, generating the probability distribution of prediction errors, and carrying out the prediction of the prediction errors; load characteristics and wind-solar power generation volatility in the park are dynamically adapted, and prediction errors are remarkably reduced; in addition, in combination with real-time monitoring and an event-driven mechanism, abnormity of a load or equipment state is rapidly responded, energy storage discharge is dynamically adjusted, standby equipment is started or a load plan is modified, and stable operation of the system is ensured. Real-time energy assessment and transaction further efficiently utilize complementary energy, and can quickly make up for the energy gap, thereby enhancing the flexibility and economy of park comprehensive energy management.

The invention discloses a measurement error prediction method and system for a current transformer, equipment and a storage medium, and the method comprises the steps: firstly collecting the error data of the current transformer under different test conditions to construct a feature data set, then constructing an error prediction model, training the model through the feature data set, and carrying out the prediction of the error of the current transformer. And finally, based on the current current data, the environment data and the trained model, predicting to obtain a measurement error of the current transformer. The model is trained by taking the environmental data as the features, so that the model can learn the influence of the environmental factors on the measurement error, the model prediction error generated by the influence of the environmental factors is reduced, and the prediction accuracy of the measurement error result is improved. And the hyper-parameters of the model are optimized by adopting a chaos guide adaptive algorithm, so that the optimization speed and the optimization precision of the model parameters are improved, the model network can quickly adapt to the influence of environmental factors on the measurement result of the current transformer, and the prediction accuracy is further improved.

The invention discloses an energy storage battery health state prediction method, device and equipment and a medium, and the method comprises the steps: collecting operation data of a target battery, carrying out the preprocessing of the operation data, obtaining battery data, and dividing the battery data into a training set and a test set; establishing a Transform model to perform health state prediction on the target battery; a genetic algorithm is adopted, and an optimal hyper-parameter combination of the Transform model is determined; according to the optimal hyper-parameter combination, using the training set to train a Transform model; and inputting the test set into the trained Transform model to carry out health state prediction so as to obtain a health state prediction value of the target battery. According to the method, the prediction precision can be improved, the complexity of model training is reduced, and the accuracy of health state prediction is improved.

The invention discloses a video anomaly detection method based on a multi-modal condition diffusion model, and belongs to the technical field of video anomaly detection. The method comprises the following steps: preprocessing a video to generate a video space-time cube and an optical flow space-time cube; constructing a diffusion model, and obtaining an abnormal score; performing weighted calculation on the reconstruction anomaly score and the prediction anomaly score; and evaluating the obtained final anomaly score to complete video anomaly detection. According to the invention, through a multi-task combination mode of optical flow reconstruction and video frame prediction, abnormity is easier to distinguish; multi-modal data are fused, feature information of more dimensions is captured, and the accuracy of anomaly detection is improved; the model is more stable when processing noise and complex backgrounds, and the stability of anomaly detection is improved; the reconstructed optical flow is used as a condition for guiding future frame prediction, the prediction error between an abnormal prediction frame and an original frame is expanded, the prediction error between a normal prediction frame and the original frame is reduced, and the training effect and performance are ensured.

The invention discloses a super capacitor energy storage frequency modulation optimization method and device, a medium and a program product, and belongs to the technical field of wind power generation. According to the method provided by the invention, the wind speed sequence is traversed, and the subsequences with similar trends are identified and merged, so that the prediction accuracy is improved; meanwhile, the method comprises an intelligent judgment mechanism, when it is judged that an excessive merging phenomenon exists in a merging result, splitting processing is carried out on a wind speed sequence, prediction and error evaluation are carried out again, and prediction errors are further reduced by adjusting a data structure in the wind speed sequence. By adopting the method, the accuracy of wind speed prediction can be effectively improved, and the prediction error is reduced, so that the coordinated operation capability between a wind power plant and a power grid is optimized, and the stability and reliability of a power system are improved. In addition, the method is also combined with the quick response capability of the super-capacity energy storage system, the error between the actual emitted power and the predicted power of the wind power plant can be made up, and the credibility of power grid dispatching is further improved.

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 construction site off-grid power supply optimization system based on big data fusion analysis, and belongs to the field of construction site power supply management. Comprising an acquisition preprocessing module, an arrangement fusion module, a graph construction module, a demand prediction module, a resource optimization module, a simulation evaluation module, a decision support module, a real-time monitoring module, a feedback adjustment module and a report analysis module. According to the invention, under the condition of different environments and demand changes, rapid response can be ensured, the power supply stability can be maintained, the overall energy utilization efficiency can be improved, a good operation state can be maintained in case of emergencies, the robustness and reliability of a power supply system can be enhanced, prediction errors can be reduced, and the overall prediction precision of the system can be improved. The method helps to better understand the relationship between past and future demands, enhances the prediction effectiveness, reduces the demands of manual intervention, and improves the intelligent level of the system.

The embodiment of the invention provides an exchange rate prediction method and device of an LSTM network model based on wavelet transform, and is applied to the technical field of data processing. Performing normalization processing on the historical exchange rate data in the historical cycle time period to obtain historical exchange rate data; according to the non-stationary and non-linear characteristics of the foreign exchange rate, analyzing a time sequence of the normalized historical exchange rate data according to a wavelet transform expansion to obtain a plurality of decomposed subsequences, and reducing the influence of the non-stationary and non-linear characteristics of the time sequence of the historical exchange rate data on a prediction result; the LSTM network model obtains an initial prediction sequence based on the decomposed sub-sequence; and synthesizing the initial prediction sequence into a target prediction sequence according to a wavelet transform synthesis formula, and obtaining a prediction result, the target prediction sequence being exchange rate prediction data in the current period time period. Therefore, foreign exchange rate prediction is realized, exchange rate prediction errors are reduced, and exchange rate prediction accuracy is improved.

The invention belongs to the technical field of building cooling load prediction, and discloses an airport terminal cooling load prediction method and a related device, and the method comprises the steps: collecting main influence index real-time data affecting the terminal cooling load prediction; inputting the collected main influence index real-time data influencing the airport terminal cooling load prediction into the ISSA-GRNN-GRU hybrid prediction model, and outputting to obtain an airport terminal cooling load prediction result; wherein the construction process of the ISSA-GRNN-GRU hybrid prediction model comprises the following steps: acquiring influence constituent element data of terminal building cold load prediction, and training and combining a pre-constructed generalized regression neural network and a pre-constructed gating circulation unit neural network by adopting an improved sparrow search algorithm to obtain the ISSA-GRNN-GRU hybrid prediction model; the overall prediction error can be reduced, and the reliability of the prediction result is improved.

The invention discloses a load forecasting and scheduling method adaptive to heat user behaviors, relates to the technical field of load forecasting, solves the technical problem that all factors influencing the load are difficult to capture when a linear method is used for processing complex problems in the prior art, and specifically comprises the following steps: collecting historical load data of a source side pipe network, and analyzing a heat user behavior mode; establishing a pipe network load simulation model based on the historical load data and the heat user behavior mode; simulating a real-time operation state of a pipe network by using a digital twinborn technology, and updating heat user behavior data in real time; according to the simulation result and the heat user behavior data updated in real time, the load change of the pipe network is predicted in real time; according to the prediction result, self-adaptive regulation and control scheduling is carried out so as to adapt to the real-time changeable switching requirements of the heat users; and the regulation and control strategy is optimized through a machine learning algorithm, and optimization of a scheduling plan is ensured.

The invention relates to the technical field of new energy power generation, in particular to a new energy power generation power prediction method and system considering turning meteorological conditions. Acquiring weather forecast and historical power data, performing feature extraction by using the Grubrum angle field data conversion and the efficient channel attention convolutional neural network, and constructing a turning weather recognition model; performing turning weather classification and historical similar day matching by utilizing a Gaussian mixture model and a mahalanobis distance according to a judgment result of the turning weather recognition model, and formulating a turning weather compensation strategy; constructing a power prediction fusion model based on a random forest model limit gradient boosting model and a long short-term memory neural network; and combining an output result of the power prediction fusion model with a compensation sequence of a turning weather compensation strategy to obtain a new energy power generation power prediction result. The problems of low efficiency and poor dynamic adaptability of turning weather feature extraction are solved, the prediction precision under the weather turning condition is improved, and the method has good prediction precision and generalization ability.

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.

The invention relates to a power grid frequency modulation and peak regulation method and device based on gravity energy storage, and a medium. The method comprises the following steps: training an ARIMA model and an LSTM model by using historical load data of a power grid and historical compensated electric energy; monitoring a load state of a power grid according to a real-time load monitoring module, and predicting electric energy needing to be compensated by using an ARIMA model and an LSTM model according to the load state; according to the electric energy which is predicted by the ARIMA model or the LSTM model and needs to be compensated and the multiple linear regression model, an environment correction coefficient is obtained, a control signal that the height of the heavy object needs to be adjusted is obtained, and according to the control signal and real-time feedback of the real-time load monitoring module, the electric lifting device is controlled to adjust the height of the heavy object; the weight is released, and potential energy generated in the weight falling process is converted into electric energy through an energy conversion device; and the generated electric energy is transmitted to the power grid through the power transmission device to realize peak regulation and frequency modulation of the power grid.

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.