685 results about "Perceptron" patented technology

The invention relates to the technical field of management and control systems, in particular to a digital intelligence system applied to collaborative management and control of water-power engineering construction participating units, which comprises a project data management module covering water-power engineering construction full life cycle management, extracting multi-source information data from various subsystems, and constructing a project view and a project management information base; the project risk analysis module is used for constructing an intelligent risk analysis model for risk analysis and generating a dynamic evaluation result, a graded early warning notification and an auxiliary decision scheme; the project collaborative management module is used for processing graded early warning notification and auxiliary decision-making schemes by using a multi-layer perceptron model, and generating finalizing service achievements and management process records; and the project document management module is used for performing compliance automatic checking and processing on the electronic documents needing to be archived, and dynamically updating and optimizing the project management information base. Through the closed-loop management and control system, the cooperative management and control efficiency of water-power engineering construction participation units is improved.

The invention relates to the technical field of medical image segmentation, in particular to a few-sample medical image segmentation method based on edge perception multi-prototype learning, and the method comprises the steps: inputting support and query images into a feature encoder, and extracting support and query feature maps of different sizes; inputting into a local attention fusion prototype generator to generate a support foreground prototype; processing the support mask through dynamic corrosion operation to generate an inner boundary prototype; generating a multi-foreground local prototype through a multi-layer perceptron; local and global information is optimized through multi-scale feature extraction, and a multi-scale prototype is obtained; fusing to obtain a multi-prototype foreground prototype; dynamic calculation weighting is carried out on the multi-prototype foreground prototype by using a double-stage prototype optimization network, and automatic calibration is carried out; then prediction is carried out through a prototype prediction module, and finally collaborative optimization is carried out through a loss calculation module; the method can effectively solve the problem of edge detail loss involved in the background technology.

The invention provides an ocean wind field prediction method based on a neural network, and belongs to the technical field of ocean wind field prediction.The method comprises the steps that sparse ocean observation data are collected, a spatial covariance matrix is established, the spatial covariance matrix is converted into a graph structure, and then multi-hop neighborhood feature aggregation is conducted through a graph convolutional network; a tensor decomposition algorithm is combined for modeling high-order feature interaction to generate a gridding wind field, a bidirectional long-short-term memory network encoder is used for extracting space-time invariant features, a multi-layer perceptron predictor is used for directly mapping a future multi-step wind field, and a course learning strategy and a Shenchang differential equation boundary layer are matched for correction. The technical problem that sparse ocean observation data are difficult to accurately reconstruct into a high-resolution gridding wind field is solved.

The invention belongs to the technical field of machine learning and pre-stress, and discloses a pre-stress release loss value prediction method and system based on machine learning, and the method comprises the steps: carrying out the multi-working-condition modeling and simulation of a pre-stress beam through finite element numerical software, and extracting the working parameters and design parameters of the pre-stress beam, carrying out data preprocessing, distribution check and feature importance analysis to obtain an initial data set; dividing the initial data set into an initial training data set and an initial test data set, and processing the initial training data set and the initial test data set to obtain a processed training data set and a processed test data set; constructing a full-connection multi-layer perceptron neural network model, defining training, verification and monitoring functions, training the full-connection multi-layer perceptron neural network model by using the processed training data set, and testing the trained model by using the processed test data set to obtain a prediction model; real parameters of the prestressed beam are obtained, the prediction model is used for predicting the prestress release loss value, and a prediction result is obtained.

The invention relates to the technical field of wind power generation, and discloses a wind power plant wind speed correction method and system based on dynamic space-time modeling, and the method comprises the steps: obtaining a whole power curve, obtaining the whole wind speed of a historical period, and constructing a multi-modal training data set; inputting a convolutional neural network to extract local features, inputting a long-short-term memory network, calculating the correlation of each time step feature, obtaining an attention weight, and finally obtaining global feature representation; setting two multi-layer perceptron branches to carry out wind speed prediction correction to obtain a common weather branch prediction value and an extreme weather branch prediction value; constructing a correction curve of each sector and obtaining a correction curve prediction value; and according to the common weather branch prediction value, the extreme weather branch prediction value and the correction curve prediction value, carrying out weighted fusion to obtain a final wind speed correction value. According to the method, the correction precision and robustness are improved, and the interpretability and applicability of the model are enhanced.

The invention belongs to the technical field of three-dimensional scene reconstruction, and particularly discloses a multi-view three-dimensional Gaussian densification method and system for adaptive density control, and the method comprises the following steps: collecting a multi-view original image, and carrying out the preprocessing of the multi-view original image; complexity features are extracted, a pixel-level complexity heat map is generated, and a globally unified three-dimensional complexity field is constructed; performing back projection on the reconstruction residual error, high-frequency inconsistency and depth / geometric consistency cost of each view angle, generating three-dimensional error popularity, determining a candidate newly-added set and a candidate pruned set, generating a weak label to train a lightweight multilayer perceptron classifier, outputting a ternary probability corresponding to newly-added / pruned / maintained, and obtaining a new / pruned / maintained three-dimensional perceptron classifier; and performing Gaussian densification operation on the newly added region. By adopting the technical scheme, fine point adding is carried out on the complex area, effective pruning is carried out on the simple area, and meanwhile, the synthesis quality, the global consistency and the calculation efficiency of the new view angle are improved.

The invention provides a marketing activity index prediction model establishment method, which belongs to the technical field of large models, and comprises the following steps: collecting multi-modal data such as text description, propaganda images and feature parameters of marketing activities, and respectively extracting text, visual and activity feature vectors by adopting a BERT model, a ResNet-50 model and a structured encoder; a four-channel bidirectional attention mechanism is constructed to realize deep interactive fusion of multi-modal features, a static prediction module is constructed to realize total index regression prediction through a stacked residual multi-layer perceptron, and a dynamic prediction module is constructed to realize dynamic prediction of time sequence indexes by adopting unified naming multi-scale time sequence feature extraction and a double-branch cooperation mechanism. And a dynamic fusion output layer is designed to generate a dynamic weight matrix through cross-modal attention to adaptively fuse static and dynamic prediction results, so that the technical problem of low prediction precision caused by insufficient marketing activity multi-modal data feature fusion is solved.

The invention relates to the technical field of supply chain risk early warning, in particular to a supply chain risk early warning method based on deep learning, and the method comprises the steps: obtaining supply chain data, extracting a material circulation relation between supply chain nodes, constructing a node relation graph, and employing a graph neural network to achieve the aggregation of the features of the nodes and adjacent nodes. And space correlation characteristics in the global network are extracted, and a multi-stage transmission and diffusion path of the risk is effectively modeled. And then, splicing node space features and historical time sequence features, inputting the spliced features into a long-short-term memory network, dynamically capturing the evolution trend of node risks along with time, identifying periodic fluctuations and sudden anomalies, and improving the prediction precision of the risk trend. And finally, a multi-layer perceptron is adopted to carry out nonlinear mapping and feature fusion on risk time sequence features output by the long-short-term memory network, node risk scores are generated, real-time early warning of high-risk nodes is realized accordingly, and the accuracy and timeliness of supply chain risk monitoring are greatly improved.

The invention provides an intelligent inspection risk assessment method and system based on multi-sensor fusion, and the method comprises the steps: obtaining original multi-source data of a transformer substation, the original multi-source data comprises a binocular vision image, infrared thermal image data and environment sensor data, and carrying out the time-space calibration and preprocessing of the original multi-source data, obtaining a multi-source sensor data stream; carrying out feature extraction on the multi-source sensor data stream to obtain a multi-modal feature set, generating a refined semantic mask based on the multi-modal feature set, constructing an initial scene relation graph, calculating a risk level based on a multi-layer perceptron classifier, and generating a risk level evaluation result and a risk distribution graph; outputting a safety distance violation warning and a risk area identifier; and generating comprehensive risk early warning information based on the risk level assessment result, the risk distribution diagram, the safety distance violation warning and the risk area identifier.

The invention discloses a traffic flow prediction method and system based on multi-scale dynamic decomposition and a space-time Transform. According to the method, firstly, an original traffic flow sequence is decomposed into trend components and seasonal components; then, modeling the trend components by adopting a multi-layer perceptron to capture global changes; and meanwhile, a space-time Transform is used for modeling seasonal components, and the architecture effectively extracts dynamic space-time dependence characteristics by integrating space-time adaptive embedding and an adaptive Switch GLU gating mechanism. And finally, fusing trend and seasonal feature representation to generate a prediction result. According to the method, noise is effectively separated through decomposition, linear enhancement space-time self-adaptive embedding, a self-adaptive Switch GLU gating mechanism and a unified space-time self-attention Transform architecture are integrated, the modeling capacity for complex space-time dependence is enhanced, prediction precision and robustness are remarkably improved, and the method can be widely applied to the field of intelligent traffic management and control.

The invention discloses a laser radar multi-gas identification method and system based on deep learning, and relates to the technical field of laser radars, and the method comprises the steps: employing a differential absorption laser radar to emit lasers with different wavelengths, receiving echo signals after gas absorption, extracting multi-domain features, and employing multi-target optimization in combination with feature importance evaluation to screen key features; generating a gas portrait of the potential gas according to the key features, and obtaining the type of the interested gas; constructing a graph structure according to the gas portrait and the gas feature similarity, performing representation learning on the graph structure by using a graph convolutional network, enhancing the graph convolutional network by using an interested gas category, constructing a gas category branch, and outputting a gas category; gas concentration branches are built by adopting a multi-layer sensor, final node representation output by the graph convolutional network is used as input, and each gas concentration is output by using linear regression. According to the invention, the distinguishing capability of small difference gases is improved, and high-precision identification and concentration inversion of multi-component gases are realized.

The invention relates to a power construction deviation degree diagnosis method based on multi-modal time series data fusion, and the method comprises the steps: collecting voltage, current and frequency data through a multi-channel synchronous sampling technology, filling missing data through cubic spline interpolation, and constructing an initial data matrix; based on a hierarchical feature extraction technology, mapping the voltage frequency domain features and the current time domain statistical features to a unified feature space through canonical correlation analysis, and generating a multi-modal feature vector with a time sequence tag in combination with a sliding window; analyzing the dynamic trend of the electrical variable under multiple time scales by adopting a long short-term memory network, and capturing a key time point through an attention mechanism; a sudden change point and a stationary section are defined, an isolated forest algorithm is combined to detect an abnormal point location deviating from a trajectory, anomaly is classified as transient disturbance or continuous deviation through a multi-layer perceptron, the evolution trend of regional continuous deviation is predicted, and the key problem of the power deviation degree diagnosis capability is improved.

The invention discloses a multi-modal data fusion financial service information processing method and system, and the method comprises the steps: S1, collecting enterprise text data and enterprise table data, and carrying out the preprocessing, and obtaining cleaned text data and cleaned table data; s2, calculating an enhanced text embedding vector, extracting a semantic representation feature vector, decoding a text triple set, and generating a text semantic vector; s3, calculating a standardized field set and a row-level entity primary key, outputting an exception risk vector, and then calculating a table exception mark and a table semantic vector; s4, calculating an entity alignment gate, and calculating an entity alignment feature vector; calculating an entity matching degree score, and finally calculating a cross-modal conflict mark; and S5, calculating a cross-modal fusion vector through the multi-layer perceptron, and then calculating a knowledge representation vector. According to the method, the problem of low entity alignment precision caused by data structure difference, field semantic conflict and insufficient entity recognition precision of a traditional method can be solved.

The invention relates to a time sequence prediction method based on Transform, and belongs to the field of artificial intelligence time sequence prediction. The method comprises the following steps: preprocessing multivariable time series data, and performing season trend decomposition to obtain a season term and a trend term; predicting the trend term by adopting a linear model; a multi-layer perceptron (MLP) and a graph convolutional network (GCN) are adopted to model correlation among seasonal item variables; using convolution kernels of different scales to extract multi-scale features of seasonal items; constructing a position code and a timestamp code; a Transform-based encoder is constructed, and a sparse attention mechanism is adopted to capture a global dependency relationship of the time sequence; capturing a short-term dependency relationship of the time sequence by adopting a local attention mechanism; initializing a decoder input; and a decoder based on a Transform is constructed to realize prediction.

The invention relates to the technical field of artificial intelligence, in particular to a sheet part performance rapid prediction method based on deep learning, which comprises the following steps: collecting multi-working condition simulation data to generate a training sample, constructing and coding a grid topological structure to extract multi-dimensional features, and inputting a perceptron to predict stress and evaluate errors after feature fusion and self-attention mechanism processing. According to the method, a structured training sample set is constructed by introducing simulation information, a geometric structure feature set is formed by combining node space coordinates, boundary constraints and a connection relation, so that mutual positions and constraint conditions among nodes are completely expressed in a graph structure, and through node-level feature extraction and feature fusion processing, a graph structure is obtained. According to the method, deep embedding of node geometric layout and boundary interrelation is realized, learnable expression of a stress evolution path in a space structure is established through local subgraph and context analysis, a multi-layer feature aggregation and attention mechanism is introduced in a node graph embedding process, and feature response expression of a key area is enhanced.

The invention provides a snakelike robot control method based on hierarchical reinforcement learning in a highly limited environment, and the method specifically comprises the steps: building a highly limited complex terrain simulation scene, and determining a task to be executed; a snake-shaped robot simulation model is established, and modeling is conducted on the structure, joints and kinetic parameters of the snake-shaped robot; a hierarchical control structure comprising a high-layer strategy network and a low-layer strategy network is designed based on a multi-layer perceptron and is respectively used for generating a global navigation sub-target and controlling specific actions; defining a state space, an action space and a reward function; in the training stage, a PPO algorithm is adopted to optimize the high-layer strategy and the low-layer strategy respectively, repeated tests are conducted through a simulation environment, strategy convergence is guided through a reward function, and finally the robot completes path planning and motion control only by depending on the motion state of the robot and obtaining the target position. Autonomous navigation and obstacle avoidance of the snakelike robot are realized, and the environmental adaptability of the snakelike robot is enhanced.

The invention discloses a command and control system resource trend prediction method based on fusion of long and short time sequence characteristics. The method comprises the following steps: acquiring a public power load or similar time sequence monitoring data set, and preprocessing the data in the data set; a deep learning network model based on a TCN-Transformer hybrid model is constructed, a TCN model and a Transformer model are adopted for parallel computing to achieve feature extraction, the TCN model extracts short-term information, the Transformer model extracts long-term features, then fusion features are obtained through a cross attention mechanism and multi-layer perceptron (MLP) weighting, and finally prediction output is generated through full connection layer mapping. Taking data in the training set as input, training the constructed TCN-Transform hybrid model, and continuously optimizing the model until convergence meets a set requirement; and performing prediction by using the trained network model. According to the method, the TCN-Transform hybrid model is constructed, so that local fine-grained features are reserved, the global time trend is effectively captured, and the accuracy of command decision making is improved.

The invention discloses a visibility regression prediction method based on multi-modal transfer learning and time coding, and relates to the technical field of artificial intelligence. The method comprises the following steps: S1, dividing a data set in different periods according to illumination characteristics, and splitting each period into a training set, a verification set and a test set; s2, preprocessing the data set; s3, constructing an initial model containing a pre-training deep learning network, a time coding module and a multi-layer perceptron regression head; s4, extracting image visual features and time feature vectors; s5, fusing the features and inputting the features into a regression head for prediction; s6, carrying out scheduling training by using layered parameter freezing, an AdamW optimizer and a dual learning rate, and combining with a mixed early stop strategy until convergence; and S7, evaluating the test set to determine a final model. According to the method, complementarity of image and time information is mined, high-precision prediction is realized, generalization is good under different illumination conditions, a layering strategy and an optimization mechanism guarantee stable and efficient training, a multilayer perceptron combination technology enhances expression, and overfitting is effectively prevented.

The invention discloses a TR assembly gold wire bonding process parameter prediction method based on a multilayer perceptron neural network, and belongs to the technical field of microwave device intelligent manufacturing. According to the method, an intelligent mapping model of gold wire bonding geometric parameters and radio frequency performance is constructed by fusing a multi-layer perceptron neural network and parameterized electromagnetic simulation. The method specifically comprises the following steps: generating 45 groups of samples in a process parameter space by adopting Latin hypercube sampling; obtaining an S parameter data set through batch processing electromagnetic simulation; box-Cox conversion and normalization preprocessing are carried out on the data; the method comprises the following steps: constructing an MLP neural network model of a 3-32-16-2 structure, and determining hyper-parameters by using Bayesian optimization; and after training is completed, rapid reverse mapping from target performance to process parameters is realized. According to the method, the number of traditional tests is reduced from more than 200 to 45, the predicted root-mean-square error of S21 is smaller than or equal to 0.12 dB, the determination coefficient is larger than or equal to 0.96, and the parameter backstepping time lt is obtained; according to the method, full-process automation from simulation, training, optimization to production and issuing is realized, and the development efficiency of the TR component is remarkably improved.

The invention discloses a network security data analysis system and method based on artificial intelligence, and relates to the technical field of network security, and the method comprises the steps: constructing a structured triple, mapping the structured triple into graph nodes and edges, and storing the graph nodes and edges in a graph database; extracting graph data from the graph database, and generating a node feature matrix, an adjacent matrix and an edge feature matrix; utilizing a graph attention mechanism to train a node representation vector, and constructing a semantic propagation matrix; obtaining a predicted attack path model; generating a candidate attack path set; constructing a credible scoring model, training and optimizing, and screening high-credibility paths with scores exceeding a threshold value; constructing a multi-layer perceptron model to obtain an attack source prediction model; according to the method, real-time atlas data is obtained, the nodes exceeding the threshold value are marked as risk nodes, the risk nodes are uploaded to a protection system to trigger alarm and check, and the automation and real-time performance of attack source recognition are improved.

The invention discloses an intelligent creative design system based on a diffusion model. The system comprises sketch generation, sketch optimization, style migration and high-quality rendering. According to the method, the design efficiency and innovativeness are improved through an intelligent technology, and meanwhile, the personalized requirements of users are met. According to the system, firstly, diversified design sketches are generated through a diffusion model, an initial sketch is generated through Gaussian noise step-by-step iteration, and keywords or style labels input by a user are introduced in combination with a conditional diffusion model so as to control the generation direction; performing feature extraction on the sketch by using a convolutional neural network, and optimizing lines and structures of the sketch through a multi-layer perceptron; then, in combination with a style migration technology, migrating an artistic style specified by a user into the sketch, and meanwhile, introducing an attention mechanism to ensure local detail consistency of style migration; and finally, performing high-quality rendering on the design by using a GAN, optimizing detail performance through adversarial training of a discriminator and a generator, and improving the image resolution by using a super-resolution technology.

The invention relates to a reinforcement learning verification reward-based big language model log anomaly automatic detection method and device, and the method comprises the steps: 1, training a big language model-based log anomaly automatic detection model, dividing a log sequence through a sliding window, and carrying out the log analysis through a log analyzer, inputting a large language model based on a Transform encoder to perform semantic extraction, using a multi-layer perceptron and a large language model based on a Transform decoder to perform dimension mapping, and inputting a large language model based on supervised fine tuning and reinforcement learning fine tuning to perform reasoning analysis to obtain anomaly detection information; and 2, performing reasoning detection by using the automatic anomaly detection model based on the large language model obtained in the step 1. According to the method, the excellent semantic extraction capability of the semantic vector extraction module and the text generation capability of the reinforcement learning optimization module are fully utilized, redundant information is further removed, the memory requirement of the reinforcement learning optimization module is reduced, and the calculation efficiency is improved.

The invention provides a petroleum drilling geomechanical characteristic estimation method based on deep learning, and aims to solve the problems of high measurement cost and poor real-time performance of shear wave velocity Vs in traditional geomechanical analysis by fusing logging while drilling LWD data and real-time drilling engineering parameters. According to the method, a Transformer model is adopted to carry out depth correction on logging-while-drilling data, depth offset of a drill bit and a logging sensor is eliminated, and real-time drilling parameters such as torque T, bit pressure WOB and drilling speed ROP are combined and input into a multi-layer perceptron MLP network to predict the shear wave speed Vs. And further calculating key geomechanical parameters based on the predicted shear wave velocity Vs. In practical application, the method improves the shear wave velocity prediction accuracy to 97.2%, the mean absolute error MAE of the shear modulus is reduced from 0.186 to 0.059, and the bulk modulus is reduced from 0.189 to 0.040. The method can be used for outputting stratum elastic parameters, optimizing bit pressure, pre-warning well wall instability and adjusting a well track, well drilling safety and efficiency are improved, and meanwhile dependence on an expensive well logging technology is reduced.

The invention provides an intelligent isotope gas concentration regulation and control method based on concentration prediction, which comprises the following steps: collecting multi-dimensional data in a closed box subjected to emptying treatment to construct a standardized control-response data sequence; predicting the isotope gas concentration through a neural network model to obtain the predicted isotope gas concentration and a long-term concentration drift trend estimation item; inputting the current standardized control-response data sequence into Transform to obtain current state representation, and generating a control instruction of valve opening and closing time and sucking pump power on the basis of the current state representation in combination with predicted concentration and target concentration; executing the control instruction, comparing the actual concentration with the predicted concentration, and coding the deviation into a deviation risk code through a multi-layer perceptron in combination with the control instruction; receiving the current state representation and the deviation risk code to establish a state-deviation mapping model, fitting deviation distribution parameters of the control deviation, and accumulating control experience to optimize a long-term strategy.

The invention relates to a wind power cluster short-term power prediction method and device of a space-time diagram neural network fused with physical information and computer equipment, and the method comprises the steps: obtaining related information data of each wind power plant in a wind power cluster, and carrying out the preprocessing; forming a physical prior data set through an engineering analysis model fusing the wake flow analysis model and the blocking effect model; taking each wind power plant as a node of the graph, constructing graph structure data for predicting the power of the wind power plant, and forming a dynamic adjacent matrix; constructing a space-time diagram neural network WB-STGNN model architecture comprising a diagram convolutional neural network module, a gating time convolutional network and a multi-layer perceptron; the method comprises the following steps: pre-training by using a physical prior data set, and then performing formal training based on historical power data and a dynamic adjacency matrix to obtain a space-time diagram neural network WB-STGNN model; inputting the wind speed of the prediction day, and predicting the active power of the whole wind power cluster in 24 hours of the prediction day. By adopting the method, the precision and efficiency of wind power cluster power prediction can be effectively improved.

The invention belongs to the computer vision technology, and particularly relates to a small sample multi-modal prototype learning-oriented remote sensing scene classification method, which comprises the following steps of: acquiring RGB (Red, Green and Blue) images with category labels and text prompts of the RGB images as a support set; establishing a text prototype, an RGB prototype and a hyperspectral prototype of each category according to the support set; and extracting to-be-classified query set image features by using a pre-trained CLIP image encoder, calculating cosine similarities between the query set image features and the text prototype, the RGB prototype and the hyperspectral prototype of each category of the support set, taking the cosine similarities as input of a multi-layer perceptron, and obtaining the category of the to-be-classified RGB image through classification of the multi-layer perceptron. High-precision and high-robustness remote sensing scene classification is realized under the small sample condition, only prototype and similarity calculation is needed in the reasoning stage, and deployment and expansion are easy.

The invention provides a reservoir pressure salinity scheduling method based on a physical information neural network (PINN). The reservoir pressure salinity scheduling method comprises the following steps: Step 1, salinity prediction based on a physical information neural network (PINN) model; the method comprises the following steps: in a PINN model framework, embedding a physical law of salinity conservation into a multi-layer perceptron (MLP) artificial neural network for training so as to carry out salinity prediction; step 2, establishing an estuary water-salt model based on a three-dimensional ocean numerical FVCOM model; step 3, carrying out upstream reservoir salty water pressing and light water supplementing emergency scheduling based on salinity forecast; comprising the following steps: Step3.1, establishing a multi-objective function; and Step3.2, solving a scheduling model. According to the method, the salinity momentum conservation constraint can be considered, the salinity prediction accuracy under the influence of different upstream flows and downstream tidal ranges can be improved, and the emergency scheduling of the reservoir for the salt tide invasion of the estuary drinking water source can be carried out in combination with the scheduling model, so that the water supply safety is ensured.

The invention discloses a signal feature compression method based on codebook discrete quantization and multi-task learning, and belongs to the technical field of crossing of signal processing and artificial intelligence. In order to solve the problem that in the prior art, compression efficiency, semantic retention and calculation complexity are difficult to consider in high-dimensional IQ signal compression, high-dimensional continuous features of an original IQ signal are extracted through an encoder; carrying out vector quantization by utilizing the learnable codebook to generate a discrete index and calculating quantization loss; inputting the discrete features into a decoder branch reconstruction signal to calculate reconstruction loss, and inputting a classifier branch prediction category to calculate classification loss; processing traditional features and coding features by adopting a multi-layer perceptron, and calculating comparison loss through cosine similarity; combining optimization quantization loss, reconstruction loss, classification loss and comparison loss to train a model; and finally outputting a discrete index as a compression feature. The method realizes efficient compression and classification semantic reservation, and is suitable for wireless communication, Internet of Things and other scenes.

The invention aims to provide a washout algorithm based on a data-driven parameter prediction model, and belongs to the technical field of driving simulation, a vehicle longitudinal acceleration signal and a motion platform state parameter are acquired and input into a pre-trained multi-layer perceptron neural network model, a washout filter parameter combination adaptive to a current working condition is calculated and output, and the washout filter parameter combination is determined. And then real-time and dynamic adjustment of parameters of the high-pass acceleration filter and parameters of the low-pass inclination coordination filter along with the state of the vehicle and the motion platform is realized, so that online real-time optimization of a control instruction of the motion platform is completed, and finally optimization of driving motion feeling simulation fidelity is realized. A data-driven parameter prediction model breaks through the limitation that a traditional washout algorithm is fixed in parameter and poor in adaptability; offline optimization and online prediction are combined, and real-time performance and fidelity are balanced; the active centering controller compensates the longitudinal acceleration by using the pitch angle, improves the space utilization rate, introduces a comprehensive somatosensory error evaluation index, and accurately evaluates the somatosensory simulation effect.