319 results about "Perceptron" patented 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 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 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 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 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 belongs to the technical field of artificial intelligence, and discloses a visual large language model illusion relieving method and a related device, and the method comprises the steps: obtaining the visual perception guide direction of each multi-layer perceptron layer of a visual large language model; and obtaining a to-be-processed visual image, reasoning the to-be-processed visual image by adopting the visual large language model, and intervening potential features output by each multi-layer perceptron layer of the visual large language model according to the visual perception guide direction in the reasoning process to obtain hallucination relief reasoning output of the to-be-processed visual image. Potential features output by multiple perceptron layers of a visual large language model are intervened according to the visual perception guiding direction in the reasoning process, the illusion problem is solved in the mode that analysis is conducted firstly and then relieving is conducted, effective intervention guidance can be provided for potential feature intervention, and therefore the intervention effect is effectively improved; and finally, object illusion in the generated content of the visual large language model is effectively reduced, and the accuracy and reliability of output of the visual large language model are improved.

The invention discloses a multi-article binding recommendation method based on graph contrast learning, and relates to the technical field of e-commerce application. According to the method, through heterogeneous binding relation modeling, a user-article-binding ternary interaction graph is constructed, direct preference of a user to an article and indirect association of binding driving are jointly learned, and the problem of relation splitting caused by multi-graph splitting in AMCBR and MGCRL is solved; according to the method, light-weight projection head optimization is carried out, double-layer linear projection is adopted to replace a traditional multi-layer perceptron (MLP), the over-smoothing risk is reduced while the contrast characterization distinction degree is enhanced, and the redundancy problem caused by fusion or knowledge modules in MultiCBR and KEMB-Rec is solved; the method introduces a multi-objective loss function, combines main task recommendation loss, cross-view comparison loss and binding comparison loss, and collaboratively optimizes local and global embedding representation, is more comprehensive than EBRec single view loss, and effectively alleviates sparsity and over-smoothing problems.

The invention discloses a dementia identification method based on brain computer network space cross attention fusion. The dementia identification method comprises the steps that resting-state electroencephalogram signals are acquired, preprocessing and brain network construction are carried out, the frequency band power ratio is calculated, and a training set and a test set are divided; the brain network fuses the spatial information, and spatial features are obtained through an isotropic graph neural network and a local feature enhancement module; the frequency band power ratio is coded by a multi-layer perceptron to obtain frequency spectrum statistical characteristics; a bidirectional cross attention module is input, and complementary fusion features are extracted; and performing mixed pooling and splicing on the complementary fusion features, then sending the fused features to a Chebyshev Kolmogorov-Arnold network classifier, and outputting an Alzheimer's disease / frontotemporal dementia / health control (AD / FTD / HC) classification result and a cognitive scale evaluation score (MMSE). According to the method, the multi-feature complementary information is effectively integrated through joint modeling of the spatial diagram features and the global spectrum features, and the accuracy, stability and generalization ability of AD and FTD classification in a complex brain network are improved.

The invention relates to the field of automatic driving system test and evaluation, in particular to an automatic driving automobile intelligence degree evaluation method based on subjective and objective mapping of a large language model, and can overcome the defects of a traditional evaluation method in the aspects of objectivity, efficiency and nonlinear fitting ability. The method comprises the following steps: converting automatic driving interaction data into natural language description; constructing an automatic driving field knowledge base, and performing knowledge enhancement on the large language model to improve the understanding of the large language model on professional rules; performing quantitative evaluation on interaction data in multiple dimensions (safety, comfort, efficiency, social interactivity and influence on a traffic system) by using the enhanced large language model; a nonlinear mapping model from interaction data to intelligent scoring is established by adopting a multi-layer perceptron, and efficient and objective comprehensive evaluation is realized. The method can effectively reduce the dependence on artificial experts, improves the evaluation consistency and efficiency, and is suitable for the intelligent level comprehensive evaluation of different levels of automatic driving systems.

The invention belongs to the technical field of fetal brain age prediction, and relates to an earthworm cerebellar fetal brain age prediction method and system based on multi-modal feature fusion, an MST-Mamba segmentation network is adopted, and local-global aggregators are embedded in each level of an encoder, so that the cooperation of local detail capture and global semantic modeling is realized; meanwhile, a dynamic channel fusion device is deployed at the jump connection part of the encoder and the decoder, so that the problems of fuzzy boundary, missed division, wrong division and the like are avoided; through three parallel branches of a multi-granularity form-texture collaborative perception architecture, two types of explicit features of macroscopic geometry and topological form and implicit features of microscopic texture are synchronously extracted, and comprehensive characterization of the development features of the earthworm cerebellar part is realized; the explicit features are subjected to standardized calibration and then spliced and fused with the implicit features in the channel dimension, the problems that multi-modal feature fusion is insufficient and calibration lacks are solved, finally prediction is conducted through a multi-layer perceptron regression head, and the accuracy and stability of the brain age prediction result are guaranteed from the source.

The invention provides a multi-modal sentiment analysis model and method, electronic equipment and a medium, and the model comprises a feature enhancement module which is used for extracting original multi-modal data features through an exclusive tool, constructing a graph structure, and enhancing the graph structure through a graph convolutional network to obtain multi-modal enhanced features; the modal multi-stage balance module is used for processing enhanced features by using different multi-stage network structures and outputting multi-modal consistency representation; the modal noise reduction decoupling and specificity recombination module is used for obtaining low-noise representation through a modal noise reduction decomposer based on the global information and recombining the low-noise representation in a modal bank to generate low-noise multi-modal specificity representation; and the hierarchical fusion prediction module is used for fusing consistency and specificity representation according to single-peak, double-peak and three-peak modes, and outputting an emotion prediction result through a multi-layer perceptron.

The invention discloses a transport capacity resource integrated intelligent scheduling method and system oriented to multiple service scenes, and relates to the field of intelligent scheduling, and the method comprises the steps: constructing a hierarchical collaborative decision-making architecture comprising a macroscopic strategic layer agent and a microscopic tactical layer optimizer; and inputting the structured feature vector into a hierarchical collaborative decision-making architecture, dynamically distributing proper solution algorithms and parameters for a microscopic tactical layer optimizer according to a real-time scheduling situation by utilizing an online element learning optimizer, and outputting a pre-scheduling scheme. According to the invention, through integrated integration of multi-service scene data, comprehensive utilization of static basic information, real-time transport capacity data and prediction environment data is realized, and the data support capability of scheduling decision is improved. The feature interaction network of the multi-layer perceptron structure can accurately extract core features and provide effective input for scheduling decisions. The hierarchical collaborative decision-making architecture is combined with an online element learning optimizer, so that a solution algorithm and parameters can be dynamically matched, and a better pre-scheduling scheme can be output.

The invention discloses a mathematical modeling competition paper quality evaluation method based on multi-feature fusion, and relates to the field of natural language processing. The method comprises the following steps: carrying out total element analysis and structured segmentation on a paper to obtain an abstract, a text, an appendix and a game text; according to the text and the appendix, calculating a necessary chapter coverage rate and support material integrity to obtain a structure normative feature vector; implementing a thinking chain process of'game question task disassembly-question solving evidence extraction-coverage verification 'by utilizing a large language model according to a game question text and a main body to obtain a game question task alignment feature vector; according to the abstract and the text, performing multi-granularity semantic interaction calculation by using deep retrieval, a cross encoder and a natural language reasoning technology to obtain a semantic consistency feature vector; and outputting a prize winning probability by fusing the multi-dimensional features through a multi-layer perceptron model. According to the method, expert review decision making is comprehensively simulated, the evaluation technology blank in the field is effectively filled, and the method has good application value.

The invention discloses an error state prediction method and system for a direct-current voltage transformer, and relates to the technical field of electric power measurement on-line monitoring, and the method comprises the steps: obtaining the error data of a target direct-current voltage transformer; segmenting the error data by using a two-order sliding window; performing feature extraction on the time sequence matrix by using fast Fourier transform to obtain a time-varying feature and a time-invariant feature; fusing the time-varying features and the time-invariant features by using a multi-layer perceptron to obtain fused features; and inputting the time-varying features, the time-invariant features and the fusion features into an error prediction model constructed based on deep learning to obtain an error prediction state of the target DC voltage transformer. According to the method, through the synergistic effect of the adaptive window, feature extraction and fusion and sliding value correction, the adaptability, accuracy and stability of error prediction of the direct-current voltage transformer are improved, and more reliable technical support is provided for online monitoring and abnormal early warning of a power system.

The invention provides a method for analyzing and predicting the water stability of an asphalt mixture, which comprises the following steps of: acquiring multi-dimensional service environment, material composition and standard water stability test data, and carrying out standardization and stratified sampling division on the multi-dimensional service environment, the material composition and the standard water stability test data; a context sensing multi-layer perceptron network supporting feature parameter sharing and gradient accumulation is constructed, and the importance weight of each dimension of features under different data distributions is automatically evaluated and normalized. An attention mechanism and a graph convolutional network are introduced, association between features is quantified, and a key high-order interaction relationship is deduced; by means of K-fold cross validation and meta-learning optimization, the generalization ability of the model for new distribution data is improved, prediction accuracy and feature interpretation are improved, and intelligent analysis and proportion design of the water stability of the asphalt mixture are promoted.

A system for inverse scattering image reconstruction using implicit neural representations (INR) is provided. The system comprises a transmitter control module, a receiver acquisition module, a random spatial sampling module, a permittivity representation module implemented using a first multilayer perceptron (MLP), an induced current representation module implemented using a second MLP, a forward simulation module, a loss computation module, and an optimization module. The system is configured to emit electromagnetic signal data toward a target object, collect scattered signal data, simulate forward electromagnetic propagation, and iteratively update the MLP parameters using loss feedback. Upon convergence, the system outputs a spatial distribution of relative permittivity values to reconstruct the internal structure of the target object.

The invention relates to the technical field of employee evaluation, in particular to a high-risk industry multi-dimensional dynamic weight employee evaluation method, which comprises the following steps of: obtaining quantitative scores of three-level indexes of employees and normalizing the quantitative scores by constructing a multi-level evaluation index system; inputting the standardized score vector into a Transform-based weight learning model, dynamically learning an index weight through a self-attention mechanism, carrying out step-by-step aggregation through a multi-layer perceptron, and outputting a comprehensive score of the employee on each first-level index; inputting the comprehensive score and the historical background information into a pre-established large language model, and performing fusion analysis according to a structured template to generate a personalized evaluation text; and finally, based on the score, the text and the employee information, automatically synthesizing a visual comprehensive evaluation report containing the radar map, the information bar and the text. According to the invention, objectiveness, individuation and operability of employee evaluation are realized.

Implicit occupancy for autonomous systems include receiving a request for a point attribute at a query point matching a geographic location, obtaining a query point feature vector from a feature map. The feature map encodes a geographic region that includes the geographic location. A first set of multilayer perceptrons of a decoder model process the query point feature vector to generate offsets. Offset feature vectors are obtained from the feature map for the offsets. A second set of multilayer perceptrons of the decoder model process the offset feature vectors and the query point feature vector to generate the point attribute. The operations further includes responding to the request with the point attribute.

The invention provides a cooperative identification method and system for a power line communication signal and an arc fault, and the method comprises the steps: firstly, constructing and training a double-flow communication fault sensor comprising a sensing early-warning sub-module and an active sensing sub-module, then carrying out the communication fault sensing based on the double-flow communication fault sensor, generating a communication fault sensing data set, and transmitting the communication fault sensing data set to a server; then, a graph neural network is adopted to carry out fault mode recognition on the communication fault sensing data set, a communication fault mode graph is generated, then, communication fault positioning is carried out based on the communication fault mode graph, a communication fault positioning data set is generated, and finally, actual feedback data of the communication fault positioning data set is obtained. And the system is updated according to the actual feedback data, so that the fault diagnosis rate and accuracy are improved.

The invention discloses a 3D Gaussian sputtering human avatar reconstruction method based on attitude learning, and the method comprises the steps: initializing a scene through 3D Gaussian primitives for a monocular dynamic video, enabling the Gaussian primitives to be embedded into a triangular gravity center coordinate system under the drive of an SMPL-X grid, and achieving the one-to-one mapping from a standard space to an attitude space; a multi-layer perceptron is utilized to predict attitude-related offset, non-rigid fine adjustment is performed on Gaussian positions and shapes, and primitives are periodically added and deleted through adaptive density control to suppress redundancy. And finally, combining the Gaussian sets corresponding to the skeleton motion and the texture details, and outputting a human avatar model which can be rendered in real time. According to the method, multi-view collection is not needed, training and reasoning resource consumption is low, and consumption-level hardware deployment is facilitated.

The invention discloses a non-reference image quality evaluation method and system based on a multi-scale image structure, and the method comprises the steps: firstly, for an input image, employing a multi-branch convolutional neural network, and respectively extracting multi-dimensional quality feature representations; secondly, on the basis of multi-dimensional quality feature representation, a quality relation graph is constructed on different spatial granularities, and global pooling is carried out respectively to obtain global quality representation on different spatial granularities; and then constructing a graph attention fusion module, carrying out weighted fusion on the global quality representations on different spatial granularities, and generating a comprehensive quality representation. Finally, the comprehensive quality features are sent into a multi-layer perceptron regression head, and a final image quality score is output. And training is carried out. According to the invention, the one-sidedness of single-dimensional evaluation is avoided, the quality evaluation is more comprehensive and accurate, and the accuracy and generalization ability of no-reference image quality evaluation are improved.

The invention discloses a multi-parameter integrated online corrosion sensing method and a corrosion monitoring inductive probe, and the method comprises the steps: collecting corrosion rate, temperature, pH and flow velocity signals, completing the preprocessing, and constructing a time alignment data set; constructing a nonlinear temperature drift correction function, and compensating the corrosion rate temperature drift in combination with the temperature-sensitive coefficient; constructing a causal perceptron, extracting a stable path, and outputting an enhanced feature sequence and a parameter weight; normalizing the enhanced features, constructing sequence input, and inputting into an iTransform network for prediction; a multi-parameter membership matrix is generated, the weight is adjusted, and a fuzzy comprehensive corrosion state result is output; and fusing the prediction sequence and the state result to generate a corrosion risk level for risk early warning judgment. According to the method, online sensing, trend prediction and prospective risk assessment of the corrosion state of the industrial equipment are realized through a causal consistency evolutionary analysis and depth time sequence prediction method fusing multi-source working condition data.

The invention discloses a distributed image quality inspection system based on artificial intelligence, and belongs to the technical field of image quality inspection. A preprocessing module; the multi-expert AI model cluster is used for executing at least one analysis of copying detection, door head similarity comparison, OCR (Optical Character Recognition) field extraction, bar code recognition and date detection; the consistency verification module is used for receiving an output result of the multi-expert AI model cluster and carrying out cross validation and contradiction analysis in a unified decision-making space so as to identify a complex collaborative fraud mode; the adaptive learning module comprises an adversarial sample perceptron and an incremental learning engine, the adversarial sample perceptron is used for finding a new abnormal mode from the rejected task in a clustering manner, and the incremental learning engine is used for carrying out periodic incremental updating on the multi-expert AI model by utilizing the found abnormal mode; and a quality inspection rule engine. According to the invention, the efficiency and the automation level of large-scale image quality inspection are improved.

The invention relates to the technical field of computer vision, and discloses a camouflage target detection method based on cascade frequency domain perception and refinement feature guidance, comprising the following steps: a computer device obtains a camouflage image and extracts a multi-scale feature map; performing frequency domain separation by using a frequency domain sensing module, generating low-frequency and high-frequency characteristic sub-bands, and performing cross-hierarchy aggregation from bottom to top through a cascaded frequency domain sensor to obtain frequency fusion characteristics; receiving the frequency fusion features from top to bottom by using an aggregation guide decoding unit, performing guide refinement in combination with the deep decoding features, adapting to irregular edges by using a variable kernel convolution strategy in the refinement process, and generating a prediction feature map in combination with a partial convolution strategy; and outputting the predicted feature map as a detection result. According to the invention, through frequency domain information complementation and dynamic convolution refinement, the problem of difficult detection caused by highly similar texture of the camouflage target and the background is solved, and the positioning accuracy and edge segmentation integrity of the camouflage target are improved.

The invention discloses a high polymer material performance prediction method and system based on deep learning, and the method comprises the steps: constructing an automatic encoder, and carrying out the dimension reduction of various data of a high polymer material through unsupervised learning; optimizing a multi-mode encoder structure by adopting an automatic design mechanism; extracting a fusion characteristic value of the high polymer material by using a multi-modal data encoder; dynamic attention fusion: introducing a dynamic gating weight to adaptively distribute modal weights to input data; introducing physical constraint, and embedding molecular dynamics into back propagation; performing quantum circuit acceleration graph convolution; and predicting and outputting, mapping the fusion characteristic value to the tensile strength and elastic modulus performance indexes of the high polymer material, and realizing nonlinear regression through a multi-layer perceptron. According to the method, a material molecular dynamics equation is converted into a forward propagation kernel from a posterior constraint, the dynamic behaviors of molecules can be simulated and predicted more accurately, and the calculation efficiency is improved while the precision is ensured.

A GEMM load-oriented GPU modeling method is characterized in that through a multi-stage collaborative modeling mechanism, cache behaviors, instruction overhead and calculation intensity are deeply coupled, accurate performance prediction of GPU execution GEMM operators is realized, the method can be widely applied to scheduling optimization of GPU intensive scenes such as AI training and scientific calculation, firstly, a three-stage cache weight distribution mechanism is established, and then, a three-stage cache weight distribution mechanism is established; quantifying the contribution of the L1 / L2 cache hit rate and the DRAM bandwidth degradation factor to the effective bandwidth; secondly, an instruction-level memory access overhead correction mechanism is introduced, and the mixing precision and the real calculation strength of a sparse calculation scene are captured through dynamic parameter adjustment and optimization; then combining the calculation force peak value and the bandwidth upper limit to construct a double-boundary constraint model, and generating a theoretical performance critical value; further predicting a stream multiprocessor utilization rate based on a neural network, and quantifying efficiency loss caused by hardware resource contention through a multi-layer perceptron structure; and finally, the integration module outputs task execution time to realize end-to-end performance prediction.

The invention provides an efficient prediction method for a deformation field of a fault-crossing deeply-buried pipeline under the action of an earthquake load, and the method comprises the following steps: building a three-dimensional numerical model comprising a pipeline structure, a fault zone and a stratum, and applying the earthquake load to the bottom or side boundary of the three-dimensional numerical model in the form of an acceleration time history; generating a plurality of groups of input parameters by adopting Latin hypercube sampling, and inputting the input parameters into the three-dimensional numerical model for dynamic calculation to obtain deformation time history data; performing harmonic decomposition and time-frequency feature extraction on the deformation time history data to form a data set; establishing and training a multi-layer perceptron and Transform coupled prediction model based on the data set; and inputting to-be-predicted parameters into the prediction model to output a deformation field. Various influence factors such as pipeline geometric parameters, stratum mechanical parameters, fault geometric characteristics and seismic oscillation characteristics are considered, and the pipeline deformation response under the multi-parameter coupling effect can be comprehensively evaluated.

The invention relates to the technical field of computer graphics and image compression, in particular to a multi-moment illumination chartlet compression and decompression method based on two-dimensional Gaussian representation and a computer device.The method comprises the steps that S1, illumination chartlets at multiple target moments are obtained and preprocessed; s2, constructing a shared two-dimensional Gaussian basis set based on the low-frequency component; s3, extracting a residual error at a multi-target moment and features of a highlight and high-frequency region; s4, multi-layer perceptron network construction and two-dimensional Gaussian attribute offset modeling are carried out; s5, performing compression and storage; and S6, decompressing and rendering. The compression rate is greatly improved, the decompression speed is extremely high, the real-time rendering requirement is met, the rendering quality is higher than that of a traditional compression method, the highlight and high-frequency detail modeling capacity is high, the structure is simple, and integration is easy.