89results about How to "Guaranteed prediction accuracy" patented technology

The invention belongs to the technical field of power prediction, and discloses a new energy power prediction method and system based on multi-source meteorological data fusion, and the method comprises the steps: obtaining meteorological data from different meteorological data sources in real time or at regular time through a data obtaining module; carrying out preliminary screening and preprocessing on the obtained data by utilizing a data processing module; the data fusion module is used for integrating meteorological data from different sources by adopting an advanced data fusion algorithm to eliminate data redundancy; a mathematical model or a machine learning model suitable for new energy power prediction is constructed by using the prediction model module based on meteorological data of multi-source fusion; and the result output and evaluation module is used for outputting the prediction result to a user in a visual form, and carrying out real-time or regular evaluation on the prediction result. The method has the characteristics of diversified data sources, advanced data fusion technology, flexible prediction model construction, high real-time performance and accuracy, wide application scenarios and the like.

The invention relates to the technical field of photovoltaic power generation, and discloses a photovoltaic power generation capacity time sequence prediction method based on an improved Transform model, and the method comprises the following steps: S1, carrying out the cleaning and standardization processing of environment data and power generation capacity data; s2, extracting local features from the input multivariate time sequence data by using a convolutional layer; s3, introducing a new dynamic weight adjustment module, and dynamically adjusting the attention weight according to the correlation between the environmental factors and the power generation amount; s4, proposing a staged decoding mechanism, reducing the calculation amount through preliminary prediction and refined decoding, and paying attention to important information; s5, performing end-to-end training on the model in combination with the optimized attention mechanism and the generative decoder; and S6, using the trained model to carry out generating capacity prediction on new data, and using an evaluation index to evaluate a prediction result.

The invention relates to a machine tool thermal error modeling method based on a time convolution network and transfer learning, which comprises the following steps: data acquisition: providing a network based on an encoder-decoder architecture based on the time convolution network, inputting the acquired data of source (sufficient) and target (few) machine tools into an encoder, and carrying out data processing on the encoder; the method comprises the following steps of: coding and decoding to output a reconstruction sequence, and adding a reconstruction loss function which is calculated by the reconstruction sequence output by a decoder on a loss function with only one predicted value, so that deep features in thermal error data can be extracted through the architecture, and the prediction precision is improved; in order to achieve enough prediction precision on another target machine tool, transfer learning is used to obtain a loss function adapted to a field, the loss functions are summarized, iterative updating is repeated until deep learning regression algorithm training is completed, and accurate prediction of the thermal error value on the target machine tool is realized.

The invention discloses a method and device for predicting the geological storage amount of carbon dioxide and a medium, and relates to the technical field of carbon dioxide capture, utilization and burying. The method comprises the steps that whether a to-be-predicted oil and gas reservoir and a control oil and gas reservoir are in the same block or not is judged; if yes, inputting the geological data, the production dynamic data and the injection parameter combination scheme of the to-be-predicted oil and gas reservoir into the optimal control oil and gas reservoir carbon dioxide geological sequestration quantity prediction model to determine the whole-area accumulated carbon dioxide geological sequestration quantity of the to-be-predicted oil and gas reservoir; if not, geological data, production data and an injection parameter value range of the to-be-predicted oil and gas reservoir are obtained, migration training is conducted on the optimal control oil and gas reservoir carbon dioxide geological sequestration quantity prediction model, and a to-be-predicted oil and gas reservoir carbon dioxide geological sequestration quantity prediction model is obtained; and determining the whole-area accumulated carbon dioxide geological sequestration amount of the to-be-predicted oil and gas reservoir. According to the invention, the precision and efficiency of carbon dioxide geological sequestration quantity prediction are improved.

The invention provides a cumulative time error prediction method based on a time window LSTM model. The method comprises the steps of obtaining historical data of a sensor; performing time feature processing on the historical data, and selecting the historical data in a time window with a certain length as a time sequence; an LSTM model is used for modeling and training the time sequence, the input of the LSTM model is the sensor time error change condition in the time window, and the output of the LSTM model is the difference value between the sensor time at the current moment and the standard time; and correcting the sensor time according to a prediction result of the LSTM model. The actual time is independent from the time on the sensor, so that the time synchronization process is simplified; through selection of a time window, consumption of computing resources is remarkably reduced on the premise that prediction precision is guaranteed; through a correction mechanism, high-precision synchronization is achieved, computing resources are optimized, and it is ensured that sensor time and standard time are kept consistent.

The invention discloses an identity recognition method and system based on 3D tooth point cloud multi-feature fusion. The method comprises the following steps: collecting a 3D tooth point cloud of a to-be-recognized identity; inputting the 3D tooth point cloud of which the identity is to be recognized and each 3D tooth point cloud of which the identity is registered in the identity database into the trained tooth multi-feature fusion identity recognition backbone network, and outputting a registration result between the 3D tooth point cloud of which the identity is to be recognized and each 3D tooth point cloud of which the identity is registered; the backbone network comprises a color feature extraction module, a structural feature extraction module, a feature fusion module, a local sampling feature extraction module, a feature aggregation module, a coarse correspondence prediction module, a point cloud decoding module, a fine correspondence prediction module and a prediction registration module. And taking the identity information of the 3D tooth point cloud with the registered identity, which is optimally registered with the 3D tooth point cloud with the identity to be identified, as the identity label of the 3D tooth point cloud with the identity to be identified. According to the invention, identity recognition can be carried out based on the 3D tooth point cloud.

The invention relates to the technical field of medical information, and discloses a pancreatic cancer risk prediction method based on machine learning and multi-modal data, and the method comprises the steps: obtaining the multi-modal data of a target user; performing time sequence deduction on the molecular biological detection data to generate a virtual molecular time sequence; time sequence signals are extracted from the virtual molecule time sequence and the time sequence behavior monitoring data; calculating the dynamic coupling strength between the two time sequence signals to obtain a space-time coupling coefficient; weighted fusion is carried out on the features, and unified multi-modal feature representation is constructed; carrying out multi-modal feature representation training to obtain a special risk prediction model for the target user; and obtaining a risk quantitative score, and identifying a key risk driving factor which contributes to the score most. According to the invention, through multi-modal time sequence fusion and personalized modeling, early-stage, dynamic and explainable and evaluable pancreatic cancer risks are realized.

The invention discloses a bridge support displacement online probability prediction method, which comprises the following steps: acquiring historical monitoring data of main beam temperature and support displacement, and introducing a group of false points to construct a probability prediction model based on a flow sparse Gaussian process, according to the approximate posterior distribution and the real posterior distribution, constructing a variational free energy boundary as a lower bound of a logarithmic marginal likelihood function, deriving an optimal variational free energy boundary and optimal approximate posterior distribution, and obtaining a specific expression of a probability prediction model; when new monitoring data is obtained, new monitoring data and old historical monitoring data are summarized in combination with new false points and old posterior distribution, new approximate posterior distribution is derived, new real posterior distribution is derived by using the Bayesian theorem, then model parameters and posterior distribution are updated online, and a new specific expression of the probability prediction model is obtained. According to the method, massive health monitoring flow data of the bridge is processed based on the flow sparse Gaussian process model, online probability prediction of bridge support displacement is achieved, and the method has high engineering application value.

A regional carbon emission prediction method and system based on carbon emission flow and big data analysis, in S1 regional division, the target area is divided into multiple sub-areas, each sub-area is labeled, and the main information set and secondary information set of each sub-area are formed according to different labels; S2 performs MIC correlation analysis to rank the correlation of the features affecting carbon emissions in the sub-area secondary information set; S3 constructs a sub-area carbon emission prediction model; S4 performs simulation calculation based on the constructed carbon emission prediction models of each sub-area, and outputs the regional carbon emission prediction results. This design can not only obtain different prediction models according to different data types, so as to make differentiated predictions for different regions, but also effectively reduce the difficulty of solving through labeling processing, and shorten the regional carbon emission prediction time.

The invention relates to the technical field of laser amplification, and discloses a high-efficiency dye laser amplification technology, which comprises the following steps of using high repetition frequency ultrafast laser as a pumping source, and carrying out beam expansion and collimation on pumping light through a telescope system; the dye concentration is adjusted in real time through the microfluidic system, and the dye concentration is monitored in real time through the fluorescence detection system; a photonic crystal frequency doubling crystal is used for frequency doubling conversion; pumping light intensity, dye concentration and output power data are collected and subjected to standardization processing; performing data analysis and system parameter optimization through a deep learning model; and the temperature distribution of the laser amplifier is monitored and adjusted in real time by using an intelligent thermal management system. By integrating the high-repetition-frequency ultrafast laser pumping source and the telescope system, uniform beam expansion and collimation of light beams are realized, and the laser amplification efficiency and stability are remarkably improved. Compared with the prior art, the influence of uneven light spots on the amplification effect is effectively reduced, and the quality and consistency of output light beams are ensured.

The invention discloses a ship track prediction method based on Attention-BiLSTM, and relates to the technical field of ship track prediction. The method comprises the following steps: acquiring ship AIS data, weather data and date attribute data; carrying out abnormal data detection and elimination on the ship track data, and filling missing data after elimination; establishing a ship track feature input candidate set, selecting a feature input variable influencing the ship track, determining a delay order between the ship track and the feature input variable, and constructing a feature vector; dividing the feature vector into a limited number of sub-model action ranges, and performing boundary division and boundary estimation; and establishing a ship track prediction model for each sub-model action range, and realizing rolling prediction of the ship track through the ship track prediction model. Accurate high-quality data and reasonable action range division are provided for ship track prediction, the ship track prediction precision is guaranteed, and the purposes of ship reasonable path planning, resource optimization scheduling and marine navigation safety improvement are achieved.

The invention discloses a novel power system output regulation and control strategy generation method and device and a control system, and belongs to the technical field of power system regulation and control. The output regulation and control strategy generation method comprises the following steps: representing net load historical data corresponding to net load climbing demand uncertainty characteristics by adopting a Gaussian mixture model; the uncertainty of the net load climbing requirement is reduced; inputting the net load day-ahead prediction data corresponding to the net load historical data and the net load uncertainty scene set into a two-stage risk avoidance model based on the lowest operation cost and the worst condition risk probability assessment theory; the two-stage risk avoidance model divides the whole strategy generation process into day-ahead optimization scheduling plan generation and intra-day real-time scheduling plan generation; the day-ahead optimal scheduling plan is optimized by using the obtained intra-day real-time scheduling plan to obtain a safe and economic output scheduling strategy; and furthermore, the novel power system is controlled in different scenes to realize efficient, stable and economical operation.

The present invention discloses a full-process model of an oscillating water column device based on CFD simulation and an efficiency prediction method thereof, wherein the device comprises a numerical wave water tank and an air chamber arranged at one end of the numerical wave water tank, the side of the air chamber is connected to an air duct, a momentum source term region is arranged in the air duct, and the width of the momentum source term region is consistent with the blade width of the turbine in the device to be evaluated. The full-process model disclosed by the present invention can accurately introduce the damping effect of the air turbine into the air chamber, fully consider the dynamic coupling effect between the air turbine and the air chamber, can truly reflect the working state of the oscillating water column, and make a reasonable prediction of the full-process efficiency.

The invention provides a water return area water bloom prediction method and device, and relates to the technical field of environmental science. The method comprises the following steps: acquiring geographic hydrological data and environment monitoring data of a target hydrological environment, and constructing a three-dimensional hydrodynamic water temperature model of a backwater area according to the geographic hydrological data and the environment monitoring data; constructing a three-dimensional eutrophication model of the backwater area based on the water temperature, the water depth, the horizontal flow velocity and the wind speed obtained by the three-dimensional hydrodynamic water temperature model of the backwater area; constructing a BP neural network model based on the backwater area three-dimensional eutrophication model, and training the BP neural network model; and obtaining a water bloom risk prediction result of the water return area of the target hydrological environment by using the neural network model. According to the method, the backwater area three-dimensional hydrodynamic water temperature model is constructed, data support is provided for chlorophyll a calculation of the backwater area eutrophication model, a large number of high-frequency detection devices do not need to be added, the model operation time is greatly shortened, and the model prediction efficiency is improved.

The application discloses a tunnel or mine gushing water space-time prediction method and system coupled with a water power numerical model, and the method comprises the following steps: based on the identified and verified underground water numerical model, outputting multi-source data, complementing the missing measured data, quantifying the difference between fault and normal stratum permeability characteristics and coupling to the data system, and incorporating the tunnel or mine excavation space data. An LSTM-isolation forest-K nearest neighbor regression coupled model is constructed, and a multifunctional module is configured to realize common training of multi-scene data. The pretreated multivariate time series data is divided into a training set and a test set, hidden features are extracted through the coupled model, abnormal detection results are fused, a residual correction model is trained synchronously, and the hyperparameters and weights are adaptively optimized according to the multi-project prediction error feedback. Based on the trained coupled model, a window rolling strategy is adopted to carry out synchronous gushing water space-time prediction, residual correction is combined, and prediction data meeting the engineering precision is output. Reliable technical support is provided for engineering construction safety control.

This invention relates to a physical information neural network-based intelligent performance prediction method and system for automated multi-material fiber placement on complex curved surfaces in aircraft. The invention includes: collecting fiber placement process parameters; performing standardized preprocessing and physical constraint-enhanced sampling to obtain an enhanced training dataset; constructing a high-order nonlinear sparse regression candidate library based on the enhanced training dataset; jointly identifying piecewise smooth physical differential equation systems and their mode switching logic using sparse Bayesian regression and Hidden Markov Models to obtain an embeddable inverse mechanism model; constructing a Physical Information Embedded Generative Adversarial Network (PI-GAN); collaboratively optimizing the generator and discriminator through adversarial training to output surface defect indices and mechanical performance indices; and initiating incremental self-learning when introducing new process scenarios to achieve model self-evolution. This invention achieves end-to-end accurate prediction of multi-dimensional quality indices such as surface defects and mechanical performance.

The present application relates to a method for predicting fatigue crack propagation rate of a diffusion bonded dissimilar titanium alloy laminate structure, which is based on the mechanical properties of the constituent materials of the dissimilar titanium alloy laminate structure, i.e., the quasi-static tensile properties of the two titanium alloy plies. The method for predicting fatigue crack propagation rate of the diffusion bonded dissimilar titanium alloy laminate structure first establishes the relationship between the fatigue crack propagation rate of the laminate structure and the mechanical properties of the laminate structure, and then establishes the relationship between the mechanical properties of the laminate structure and the mechanical properties of the dissimilar titanium alloy single ply, thereby describing the relationship of the fatigue crack propagation rate of the laminate structure. Compared with the prior art, the method of the present application has the advantages of simple and convenient test, engineering precision, rapid and practical, etc., and effectively reduces the test cost and test time required for crack propagation test of the dissimilar titanium alloy laminate structure processed from different parameters.

This invention belongs to the field of industrial mechanism modeling technology, specifically involving an intelligent control method for imidazole crystallization reaction temperature based on operating condition correlation prediction. The method includes: collecting multidimensional time-series data within the batch cycle of imidazole crystallization and filtering for noise reduction; calculating the deviation between theoretical heat dissipation and the actual center temperature of the material based on thermodynamic equilibrium mechanism, obtaining the intrinsic latent heat release index of crystallization, and constructing a dynamic time-regularized distance matrix to achieve feature alignment optimization; using external electrical parameters to infer the material rheological state to obtain the apparent rheological resistance factor, which is then used as the covariance kernel function of a weighted adaptive reconstruction Gaussian process regression model; inputting the predicted trajectory into the model predictive controller to solve for the optimal command to drive valve action. This invention eliminates the temporal misalignment of operating condition variables, dynamically adapts to the nonlinear heat transfer resistance changes in the imidazole crystallization process, and achieves high-precision intelligent control of the reaction temperature.

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.

This application provides a method, apparatus, electronic device, and storage medium for predicting battery heat generation power. It acquires multiple first average heat generation powers of the battery when charged and discharged at different rates at a target temperature, and multiple second average heat generation powers of the battery when charged and discharged at the target rate at different temperatures. The target temperature includes the middle temperature of the battery's operating temperature range, and the target rate includes the rate at the middle position of the battery's rate range and the rate at the beginning and / or end positions. The multiple first average heat generation powers and multiple second average heat generation powers are fitted to obtain a relationship between the heat generation power of the battery when charged and discharged at different rates at different temperatures. Based on the obtained heat generation power relationship, the heat generation power of the battery when charged and discharged at any rate at any temperature is determined. This application can save testing time and resources while ensuring prediction accuracy.

A training data division and substandard battery cell elimination method for model training comprises the following steps: obtaining a sample proportion of each data interval according to a density value and a weight constraint condition of each data interval, and further obtaining an initial division value; after obtaining an optimal classification model and a final division value based on the initial division value, classifying the training set and the prediction set to obtain a data prediction result of each data interval; and determining a rejection upper limit and a rejection lower limit according to the interval upper limit and the interval lower limit of each data interval so as to reject the substandard battery cells. The data division is performed according to the numerical value change condition, so that the part with relatively large numerical value distribution change can be subjected to finer category division, and the finally obtained data prediction result is more close to a real value; and on the basis of the prediction precision, the rejection range of different data intervals is re-determined so as to further reject the battery cells which do not reach the standard, and the battery cells which reach the standard are reserved as far as possible, so that over-killing and missing-killing phenomena are balanced.

The present application relates to the technical field of bearing life prediction, in particular to a kind of gradient composite coating sliding bearing life prediction method, comprising the following steps: step one, build the coating degradation physical simulation model of multi-field coupling;Step two, adopt active learning algorithm to build high-precision proxy model, design active learning query strategy;Step three, online monitoring and multi-domain feature extraction;Step four, real-time state mapping and life prediction based on transfer learning;Step five, prediction result output and model updating.The present application can improve the accuracy of gradient composite coating sliding bearing life prediction, greatly reduce the operation load, and improve the prediction efficiency of bearing life.

This invention discloses a data-driven method for monitoring marine phytoplankton abundance using underwater acoustic networks. The method includes: constructing and training a multi-level spatiotemporal feature ensemble model based on historical marine phytoplankton abundance and corresponding marine environmental data to determine the optimal spatial location and monitoring time window of the underwater acoustic network nodes; constructing a teacher model for channel prediction, including a multi-layer cascaded structure and residual modules, and compressing the teacher model into a student model using spatiotemporal knowledge distillation; training the student model to achieve real-time channel state prediction at the underwater acoustic network nodes; matching suitable underwater acoustic communication devices and seasonal optimal routing strategies to each underwater acoustic network node based on the optimal spatial location and real-time predicted channel state, and performing adaptive modulation and coding at the link level, while simultaneously monitoring phytoplankton abundance using the underwater acoustic network. This invention can improve data throughput and prediction accuracy under strict energy consumption constraints.

A lightweight cloud server load prediction method based on wavelet decomposition and multi-head external attention mechanism relates to the technical field of cloud server load prediction. It effectively combines wavelet transform technology and Transformer architecture, and can quickly and accurately predict the long-term load of various resources in cloud data centers. The prediction sequence is decomposed into subsequences of different frequencies by using wavelet transform technology, highlighting the trend part of the load, and using high-frequency information to refine the prediction effect, so as to accurately capture the time evolution law of the load. By introducing the external attention mechanism module, the performance of the model is significantly improved. On the basis of maintaining the prediction accuracy, the computational complexity is reduced, thereby optimizing the reasoning efficiency of load prediction. At the same time, the external memory unit can deeply explore and utilize the potential influence between different sequences, further improving the prediction ability of the model.

The application discloses a scene generation method and device based on source-load uncertainty and equipment, and relates to the technical field of energy scheduling. The method comprises the following steps: acquiring real-time source-load data of a power system; processing the real-time source-load data based on a target prediction model to obtain a multi-time-scale source-load prediction result, wherein the target prediction model is obtained by compressing the weights of a long short-term memory network prediction model; generating a collaborative scheduling strategy of source-load resources under the multi-time-scale source-load prediction result based on a multi-agent scheduling decision model; and generating a hydropower source-load scene set of the power system at different time stages under the source-load uncertainty based on the collaborative scheduling strategy. In the foregoing manner, the prediction link and the decision optimization link are closely coupled, the multi-time-scale prediction result is used to dynamically guide the reinforcement learning decision, the adaptability of the system to the source-load uncertainty is enhanced, and the organic collaboration of the multi-time-scale and the accurate generation of the uncertainty scene are realized.

The application provides an aircraft wing skin corrosion depth prediction method based on a GAN and an improved LightGBM algorithm, which comprises the following steps: S1, data enhancement: four fully connected layers are used to extract feature information between columns; S2, an improved condition generator and discriminator are used, and parameters are updated; S3, the LightGBM algorithm is improved, and the aircraft wing skin corrosion depth is predicted. The aircraft wing skin corrosion depth prediction method using the generative adversarial network for data enhancement and the gradient regression model for sample regression can accurately predict small sample data. Meanwhile, the gradient regression model LightGBM algorithm is improved, which is more suitable for the prediction of the aircraft wing skin corrosion depth, and can greatly improve the prediction accuracy of the aircraft wing skin corrosion depth prediction.

This invention discloses a short-window gamma-gamma turbulence parameter prediction method for space-to-ground laser communication, belonging to the technical field of space-to-ground laser communication and atmospheric turbulence channel parameter prediction. This method addresses the problems of traditional methods, such as strong dependence on long observation windows, significant degradation in prediction accuracy under short-window scenarios, and insufficient robustness under low signal-to-noise ratio conditions. It establishes a short-window observation model for the space-to-ground optical link and a gamma-gamma channel statistical model, constructs time-dependent short-window training data, and designs a short-window gamma-gamma network. Temporal features are extracted through a convolutional backbone, and a scintillation exponential physical regularization auxiliary head is used to achieve joint parameter prediction under physical constraints, outputting predicted gamma-gamma distributed parameters. This method can achieve high accuracy and good stability in parameter prediction under short observation windows and low signal-to-noise ratio conditions, and can be used for turbulence channel state characterization at the space-to-ground laser communication receiver.

The application discloses a crack rock mass tunnel stability evaluation method and system based on finite elements and machine learning, and belongs to the technical field of geotechnical engineering, and comprises the following steps: acquiring tunnel geometric parameters to construct a finite element model, calculating the type I and type II stress intensity factor values of a crack tip, checking and removing abnormal data through multi-path integration, constructing an effective data set, constructing a Kan neural network model and training until convergence, inputting target tunnel parameters into the model, outputting stress intensity factor prediction values, calculating the equivalent fracture toughness of the crack tip, determining the crack initiation position based on the size relationship between the upper and lower equivalent fracture toughnesses of the crack tip, selecting a fracture criterion according to rock mass characteristic parameters, calculating the crack initiation direction and completing stability evaluation. The application realizes rapid and accurate prediction of the crack initiation behavior of a crack rock mass tunnel, and can provide a reliable basis for engineering stability evaluation.

This application relates to the field of automotive technology, and more particularly to a method, apparatus, device, and storage medium for adjusting a model. The method includes: obtaining a first collision simulation result based on a whole-vehicle collision model of a vehicle; the first collision simulation result includes at least the impact force of a first barrier and a protective beam in the vertical direction and a first acceleration of the non-deformed position of the protective beam in the vertical direction; determining the equivalent mass that generates the impact force based on the impact force and the first acceleration; controlling a second barrier included in the simplified collision model to impact the protective beam included in the simplified collision model with the impact force based on a simplified collision model of the vehicle and the equivalent mass, thereby obtaining a second collision simulation result; determining a first distance based on the first collision simulation result; determining a second distance based on the second collision simulation result; and adjusting the simplified collision model based on the difference between the first distance and the second distance.

The present application relates to a kind of computing method for realizing wide range dynamic noise map system fast update, comprising the following steps: using the data information of system database, based on existing day and night period and city road grade, simplify the structure of traditional road sound level mechanism model;Using big data technology, using hybrid modeling method, regression each grade road sound source (day and night) sound level contribution amount prediction model;Road noise fast calculation method based on sound level contribution amount prediction model is constructed, the fast update of dynamic noise map is realized.Compared with prior art, while ensuring the calculation precision, the operation and update efficiency of noise map system is greatly improved, and the technical constraints of foreign core computing software are broken, the localization of noise map core computing technology is realized, which provides technical support for wide range deep development of noise map.