4947 results about "Model parameter" patented technology

The invention discloses an electromechanical system fault pre-diagnosis method and system based on digital twinning. The method comprises the following steps of obtaining multi-source data in an electromechanical system operation process; preprocessing the acquired multi-source data, wherein the preprocessing comprises data cleaning, normalization processing and feature extraction; and on the basis of the preprocessed multi-source data, an electromechanical system design drawing, a three-dimensional geometric model, material attributes and a kinetic equation are fused, and a digital twin model is constructed. According to the invention, through a digital twin model dynamic calibration and prediction algorithm, early abnormity of the equipment is identified in advance, the fault probability and the residual life are output, and non-planned shutdown is reduced; by constructing a cross-physical domain fault feature system and fusing model simulation and actual measurement data, the potential fault identification accuracy is improved, and the missed diagnosis rate is reduced; by calibrating parameters of the digital twin model in real time, the method adapts to nonlinear changes of equipment, ensures high-fidelity mapping of the model, and improves fault prediction precision.

The present invention relates to a digital twin processing method and system, and a cloud platform. The method comprises: acquiring production system elements, carrying out abstraction definition and parameterization description on the production system elements by means of digital-to-analog knowledge fusion of a mathematical model and a mechanistic model, so as to construct a digital twin ontology model; analyzing and reconstructing model data to obtain a mapping model of which object variables can be directly accessed and operated by a collective motion control method, so that the model is visualized at the cloud; and using an external data source to drive parameter update and operation matching of the model by means of a motion control method, so as to complete cooperative deployment and synchronous evolution of an actual physical device and the model in the production process on a cloud server. According to the present invention, a model is constructed by means of digital-to-analog knowledge fusion of a mathematical model and a mechanistic model, the model is mapped to achieve motion visualization, model parameter update and operation matching on the cloud are achieved, and then cooperative deployment and synchronous evolution of a physical device and the model are completed.

The invention relates to the technical field of machining precision control, in particular to a laser carving precision control method and system.The laser carving precision control system comprises a feature collecting unit, a model building and analyzing unit, a dynamic threshold value adjusting unit and an online incremental learning unit, and the feature collecting unit collects vibration, current and temperature data through a multi-source sensor array; the model construction analysis unit realizes dynamic prediction of processing parameters by combining a bidirectional long-short-term memory network with an attention mechanism, and the dynamic threshold adjustment unit dynamically updates parameters of a numerical control system based on a material hardness real-time detection and thermal coupling model. The online incremental learning unit automatically generates training samples through error data, continuously optimizes model parameters and constructs a'data acquisition-intelligent modeling-dynamic compensation-model evolution 'closed loop, so that accurate prediction and adaptive adjustment of machining parameters are realized, and the adaptability of the manufacturing process to multi-variety and small-batch working conditions is remarkably improved.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses an enhanced decision method, device, equipment and medium based on thinking chain annotation, which comprises the following steps: extracting core information features to generate a structured data set, loading a basic language model and executing supervision fine tuning to generate a fine-tuned model, fusing multi-modal input to generate fusion features, constructing a state observation space to receive the fusion features as input, generating reward signals based on a double reward mechanism and optimizing model parameters to generate an optimized model, deploying a monitoring module to dynamically adjust parameter configuration to generate an adaptive decision model, and outputting a decision response result. According to the method, multi-source data extraction, structured expression, multi-modal fusion, reinforcement learning optimization and dynamic adaptive mechanism fusion are carried out, so that the understanding ability of the model to complex data, reasoning transparency and the adaptive ability of the model to coping with environmental changes are remarkably improved.

The invention provides a multi-modal heterogeneous model retrieval enhancement method and system, and the method comprises the steps: building a knowledge and application example double-corpus based on user multi-modal query, and designing a joint retrieval mechanism to obtain a result set; mapping and scheduling to obtain feature representation through special processing channels for texts, images and audios and a Spiking neural network with a segmented trapezoidal topological structure; constructing a three-stage cascade architecture of a basic model, an advanced model and human experts, and obtaining a decision path and answer candidate set in combination with a recursive and discarding decision mechanism; a Hamiltonian graph network is used for representing a multi-modal relation, and a gradient-free descent method is used for rapidly training and optimizing model parameters; an enhanced retrieval result is obtained through cross-modal semantic alignment and dynamic retrieval window adjustment; and high-quality response is obtained through context-aware sorting and retrieval enhanced reasoning. According to the method, the multi-modal information retrieval processing efficiency and the heterogeneous model reasoning response quality are improved.

The invention relates to the technical field of data processing, in particular to a standardized detection result calibration method based on multi-modal fusion, which comprises the following steps of: performing high-precision space-time synchronization and standardization on different modal data, performing dynamic compensation by utilizing timestamp alignment, a cross-correlation function and an IMU (Inertial Measurement Unit), and performing dynamic parameter adjustment by adopting a local abnormal factor algorithm. Carrying out cross validation by utilizing inherent relevance of multi-modal data, constructing and continuously optimizing a high-confidence system state representation model, and realizing real-time identification and self-adaptive calibration of model parameters by adopting a Bayesian online learning framework; the method further comprises the steps that closed-loop recalibration is conducted through multi-sensor cross validation and residual analysis, an optimal action sequence is generated through a reinforcement learning agent, accurate prediction of key indexes of a monitored object is achieved, instant calibration and situational prediction can be conducted according to a specific event, and the intelligent level and maintenance efficiency of system monitoring are comprehensively improved.

The invention discloses a CAD automatic generation system and method based on intelligent model selection and application, and aims at achieving automatic modeling under the multi-modal design requirement. The system comprises a user interaction module for receiving multi-modal input such as natural language, sketch and voice; the intelligent demand analysis module is used for combining an industrial large language model and a product knowledge graph, combining semantic analysis and generating a structured demand; the intelligent model selection calculation module is used for matching the optimal parameter combination and the component list based on a multi-objective optimization algorithm; the CAD automatic generation module calls a parametric modeling engine to generate an editable three-dimensional model; the constraint solving module is used for processing hard constraints and soft constraints in real time and dynamically adjusting model parameters; and the model output and interaction module feeds back a design state and supports user iteration. The system realizes full-process automation from the design intention to the CAD model, improves the design efficiency and accuracy, and is suitable for the fields of mechanical design, intelligent manufacturing and the like.

The invention provides a multi-modal large model task processing method, device and equipment based on reinforcement learning, and the method comprises the steps: employing a multi-modal large model, generating G groups of responses for multi-modal visual task data, carrying out the scoring of each group of responses, obtaining a reward value, carrying out the standardization of the reward value, obtaining a dominance score, building a strategy updating gradient through the dominance score, and carrying out the calculation of the strategy updating gradient. A final result is obtained after expectation reward maximization, model parameter adjustment and multi-round iteration, and an expectation reward objective function comprises an expectation calculation item and KL divergence constraint, so that a current strategy can be updated by comparing the performance of candidate strategies through group relative strategy optimization, and local optimum can be helped to be jumped out; the strategy updating amplitude is limited by the KL divergence constraint, and the model is prevented from violently changing in the optimization process, so that the training stability is improved; the dynamic strategy iteration allows the model to adjust the balance between exploration and utilization according to the learning progress on the basis of keeping the stability, thereby further ensuring the effectiveness and stability of strategy optimization.

The invention discloses an image processing method and device and a storage medium. The method comprises the steps of obtaining a to-be-simulated 3D convolution model and training data; decomposing the 3D convolution model into cascading of a 3D space convolution model and a 3D time convolution model to obtain a pseudo 3D cascading convolution model; training a pseudo 3D cascade convolution modelby using the training data, and obtaining parameters of a 3D spatial convolution model and a 3D time convolution model; converting the 3D space convolution model and the 3D time convolution model intoa 2D space convolution model and a 2D time convolution model; setting a feature rearrangement rule for the 2D spatial convolution model and the 2D time convolution model; mapping model parameters ofthe 3D spatial convolution model and the 3D time convolution model into parameters of a 2D spatial convolution model and a 2D time convolution model to obtain a 2D cascaded convolution model; and performing convolution operation on the image by using the 2D spatial convolution model and the 2D time convolution model. By means of the mode, image processing conducted through 3D convolution operationcan be achieved through the 2D convolution model.

A method and device for optimizing comprehensive production of an oil reservoir. The method comprises: on the basis of historical production data, performing regional level recognition, single-well level recognition, and production optimization model recognition, and updating oil reservoir dynamic recognition; on the basis of the historical production data, using the following steps to perform cyclic historical fitting on model parameters of a plurality of production optimization models: performing historical fitting on the model parameters, and performing well-to-well communication relationship calibration on the plurality of production optimization models; determining, on the basis of the updated oil reservoir dynamic recognition, a constraint condition corresponding to each production optimization model, solving an objective function corresponding to each production optimization model, obtaining an optimal decision variable corresponding to each production optimization model, and obtaining a development regulation scheme corresponding to each production optimization model; obtaining a comprehensive development regulation scheme of an oil reservoir; and updating the oil reservoir dynamic recognition on the basis of a regulation effect obtained by real-time monitoring of a development operation.

The invention discloses a remote sensing image generation method based on a federal visual language model, which belongs to the technical field of machine learning and specifically comprises the following steps: receiving text instruction description by each client; extracting a multi-scale feature map from private remote sensing image data through a visual encoder, and generating a semantic embedding vector by text instruction description through a language encoder; inputting the semantic embedding vector and the multi-scale feature map into a dynamic attention mask generator to generate pixel-level space weight distribution; carrying out weighted fusion operation on the multi-scale feature map, and generating visual feature representation of text conditionalization; generating a remote sensing image according with the description of the text instruction through an image decoder; the client uploads model parameter increments of the visual encoder, the language encoder and the dynamic attention mask generator to the central server; the central server aggregates the model parameter increments, and distributes the updated global model parameters to each client; according to the method, the flexibility and semantic consistency of remote sensing image generation are effectively improved.

The invention relates to a federated learning system based on multi-key homomorphic encryption and adaptive differential privacy, and belongs to the technical field of privacy computing. According to the system, on the premise that no trusted third party exists, a multi-client collaborative key generation and threshold decryption mechanism is achieved, it is ensured that model parameters are always in an encrypted state in the aggregation process, and leakage of a single node is prevented. By introducing a parameter sensitivity analysis and selective encryption strategy, the system only encrypts high-risk parameters, and the encryption burden is effectively reduced. Meanwhile, in combination with an adaptive privacy budget allocation mechanism, the system dynamically adjusts noise intensity according to a model training state, and model performance and convergence speed are maintained while privacy protection capability is improved. According to the method, high robustness and collusion resistance are realized, stable operation under the condition that part of clients are offline is supported, and the method is suitable for application scenes such as medical treatment and finance with high data sensitivity and strict performance requirements.

The invention relates to the technical field of business data insight, and discloses a method for disassembling insight business data through indexes. The method comprises the following steps: firstly, constructing a multi-dimensional index system, determining key business indexes and associated dimensions, and outputting an index disassembling framework; the method comprises the following steps: collecting multi-source data in a business process, performing cleaning and standardization processing, extracting index characteristics and dimension attributes, and constructing a business data association graph; introducing an adaptive weight distribution mechanism and a hierarchical iterative algorithm to optimize a preset attribution model, and generating a preliminary index contribution degree sequence based on an index disassembly framework; setting a monitoring threshold value of each dimension index, and tracking service data in real time; dynamically adjusting an index weight and a model parameter according to a tracking result, and recalculating and updating an index contribution degree sequence; and evaluating the service health degree, identifying abnormal index nodes, and starting a root cause analysis process for the abnormal index nodes. According to the method, multi-dimensional and dynamic insight of the business data can be realized.

The invention discloses an anomaly detection model selection method and system based on index portraits, and relates to the technical field of intelligent operation and maintenance of power systems. The method comprises the following steps: collecting historical data of a target monitoring index, extracting multi-dimensional features to construct an index portrait, and classifying the index portrait; screening candidate anomaly detection models from the matching rule base, performing adaptation degree scoring in combination with a model compatibility evaluation mechanism, determining an optimal anomaly detection model to perform anomaly detection, and outputting an anomaly judgment result; when a plurality of models exist, generating a final abnormal result through a confidence-driven arbitration mechanism; for multi-index abnormity, causal reasoning is carried out in combination with an electric power knowledge graph, main alarm indexes are determined, and secondary indexes are processed according to a delay strategy; meanwhile, incremental updating of index portrait features, adaptive adjustment of model parameters and dynamic optimization of matching rules are supported, and a whole-process closed-loop mechanism covering'portrait construction-model matching-result fusion-alarm decision-feedback updating 'is constructed.

The invention relates to the technical field of oil and gas field development, and discloses an ultra-large type true triaxial hydraulic fracturing fracture evolution path prediction method and system.The prediction method comprises the steps that a coupling geomechanical model integrating microscopic, macroscopic and wellbore flow scales is constructed; initializing the model and performing crack initial expansion simulation; collecting construction data in real time, and dynamically optimizing model parameters through a data assimilation algorithm; performing fracture evolution advanced prediction by using the updated model; and generating an optimization decision based on the prediction result and feeding back to the construction site. According to the method, fusion of a multi-scale physical mechanism and real-time dynamic prediction is realized, and accurate prediction and active control can be performed on crack expansion under the ultra-large true triaxial condition.

Disclosed in the present invention is a neural network-based defect detection method for gluing quality on aircraft skin. The method includes: data acquisition: taking photos of aircraft skin by using a camera to acquire image data; preprocessing the acquired image data; annotating the data by using annotation software to acquire a data set for network training; establishing a defect detection network model based on feature erasure and boundary refinement, where the defect detection network model includes a feature extraction network, a semantic-guided feature erasure module, a multi-scale feature fusion network, and a defect prediction network based on boundary refinement, which are sequentially connected, the data set is used for training the network model, and trained model parameters are saved; and detecting a directly collected skin gluing image by using the trained network model and outputting detection results.

The invention discloses a federated learning backdoor defense method based on pruning and fine tuning in the technical field of artificial intelligence and network security, the method realizes defense through two core mechanisms of dynamic pruning and gradient constraint fine tuning, and the method comprises the following steps: firstly, calculating a sensitivity score based on a neuron activation frequency and a weight outlier degree; dynamically identifying and cutting redundant neurons utilized by a backdoor, and blocking an abnormal activation path; secondly, gradient direction consistency detection and amplitude constraint are introduced in the fine tuning stage, and a malicious client is inhibited from reconstructing a back door through an abnormal gradient; the server continuously purifies model parameters and enhances robustness by cyclically executing pruning, fine tuning and aggregation operations; the method does not need to depend on an extra clean data set, strictly follows a federated learning privacy protection principle, reduces communication overhead through lightweight pruning, maintains main task performance in combination with gradient constraint, is suitable for a federated learning scene in which edge equipment participates, and effectively balances a defense effect and model stability.

The invention discloses an end-to-end fault diagnosis and identification method based on multi-modal fusion, and the method comprises the steps: 1), collecting a vibration signal and an acoustic signal, carrying out the preprocessing, and constructing a training sample set; 2) performing feature extraction to obtain a high-dimensional modal feature vector; 3) generating a sparse adjacency matrix through an end-to-end deep learning graph generation module, and establishing a graph generation structure relation; 4) constructing a multi-receptive field Chebyshev graph convolutional network, and extracting node-level features in a graph generation structure; 5) inputting the structure sensing features into a full-connection layer for mapping, and completing prediction and discrimination of a fault category to which an input sample belongs; performing model supervision training, and optimizing model parameters in an end-to-end mode; and 6) carrying out prediction output on the fault identification model on the test set, and carrying out quantitative evaluation on the fault identification result to obtain the fault identification device.The method belongs to the technical field of equipment operation state monitoring and fault diagnosis, and realizes accurate fault diagnosis of the rotating equipment.

The invention discloses a digital twinning system and a construction method thereof, and relates to the technical field of digital twinning, and the construction method comprises the steps: carrying out the classified collection of multi-source heterogeneous data of a digital twinning entity, carrying out the filtering and denoising through a self-adaptive wavelet threshold, mapping the data into a multi-dimensional feature vector, screening key features, and outputting the key feature data; the method comprises the following steps: establishing a physical mechanism layer based on a general physical rule, defining core physical parameters and a constraint equation, initializing a particle swarm to establish a data driving layer, constructing an LSTM time sequence prediction module, executing particle filter state calibration, and selecting a model to configure a communication protocol to establish a virtual-real interaction layer, so as to realize state mapping and instruction feedback of a digital twin entity and a virtual model; and calculating virtual and real state deviation in real time and performing attribution diagnosis, adjusting model parameters or key features according to a deviation source, generating an optimization parameter combination based on a long time sequence prediction result and performing simulation verification in a virtual environment, and controlling entity parameter adjustment through an instruction feedback channel so as to realize predictive optimization.

The invention relates to a deep learning model quantitative evaluation method and device and a storage medium, and the method comprises the steps: automatically determining a target quantification strategy through a preset mapping relation between a quantification strategy and model parameter information, does not need to manually debug parameters, carries out the model quantification of an optimized model based on a quantification tool chain configured by a configuration file, and improves the efficiency. According to the method, a quantitative model and a test sample are configured to obtain a deployment package, the deployment package comprises the quantitative model and the test sample, a reasoning index can be directly obtained on end-side equipment and compared with an original model index, objective quantitative evaluation is formed, and the method forms a standardized closed-loop process from model structure optimization, strategy generation and tool chain configuration to end-side deployment and evaluation. Manual intervention of connection of all links is reduced, dependence of an existing method on artificial experience is broken through, model quantitative evaluation can be automatically achieved, and the model quantitative requirement of a deep learning model is met.

The invention belongs to the technical field of tunnel safety monitoring, and particularly relates to a rock stratum stress-deformation coupled tunnel safety real-time dynamic modeling method, which comprises the following steps of: embedding a rock mass mechanical relationship into a neural network model as a hard constraint, and establishing explicit mapping of monitoring data and a physical field, a model parameter field is dynamically optimized by adopting ensemble Kalman filtering real-time general field detection data, and a data driving and physical mechanism collaborative deduction mechanism is designed, so that long-term prediction error accumulation is effectively inhibited; a stress field, a deformation field and other physical quantities deduced and output by the model are directly utilized to calculate disaster risk indexes with clear mechanical significance, and a physically interpretable early warning decision is realized by fusing fuzzy reasoning and multi-source risks; the technical problems of physical misalignment, weak long-term generalization and poor early warning interpretability of a data-driven model are solved.

The invention discloses a visual language multi-modal fusion method based on parameter-free cross attention, and belongs to the field of computer vision. The implementation method comprises the following steps: using a fixed pre-training language model as a trunk, using a visual encoder to extract image features, and calculating a cross attention weight between language query and visual features through a parameter-free activation function, replacing a plurality of groups of learnable projection matrixes introduced by a traditional cross attention module, and significantly reducing the model parameter scale. A multi-scale visual feature generation mechanism based on pooling operation is introduced, and rich visual semantic prompt information is provided for a language model. A dynamic feature selection module is designed in combination with cross attention, visual areas corresponding to all text tokens are screened, low-correlation areas are discarded, only visual content more contributing to the current language context is reserved, accurate information matching and efficient fusion between modals are achieved, and the accuracy and efficiency of information fusion are improved. And the performance of the visual language model in tasks such as image-text question answering, image generation and multi-modal instruction understanding is improved.

The invention relates to the field of recommendation systems, and particularly discloses a personalized recommendation method based on multi-modal behavior sequence modeling, which comprises the following steps of: acquiring multi-modal behavior data such as user text, image, time and place, preprocessing, and realizing dynamic fusion of the data by utilizing a multi-modal self-attention mechanism (MMSA) to obtain a multi-modal behavior sequence model; and the interest evolution of the user is accurately captured. An independent RNN module is adopted to model long-term and short-term interests of a user, and the long-term and short-term interests are combined through a self-learning weight coefficient, so that the change of the user interests is reflected more accurately. In addition, by introducing an online learning and incremental learning mechanism, model parameters are dynamically adjusted according to real-time feedback of the user, and it is ensured that a recommendation result can respond to user interest changes in time. According to the method, the defects of an existing recommendation system in the aspects of data fusion, time sequence modeling and real-time adaptability are effectively overcome, recommendation individuation and accuracy are improved, and the real-time updating capacity and scene adaptability of the system are enhanced.

The invention discloses a wire drawing machine operation state evaluation method and system based on deep learning, and relates to the technical field of intelligent manufacturing, and the method comprises the steps: deploying a multi-source sensor at a preset part of a wire drawing machine, and forming a multi-dimensional operation data flow; the deep learning evaluation model is trained based on the standardized time series data set, model parameters are optimized through a cross entropy loss function, the model learns the characteristic difference between a normal working condition and an abnormal working condition, and the deep learning evaluation model is trained based on the standardized time series data set. And inputting a multi-dimensional operation data flow collected in real time into the trained deep learning evaluation model to generate a dynamic evaluation report. According to the wire drawing machine operation state evaluation method and system based on deep learning provided by the invention, a visual operation state score can be output, detailed fault type probability distribution is given, and intelligent support is provided for equipment maintenance decision.

The invention discloses a hybrid Lp regularization magnetotelluric two-dimensional inversion method based on adaptive weight, and the method comprises the steps: reading magnetotelluric observation data, carrying out the quadrilateral finite element grid discretization of a whole inversion region, building an inversion grid, and building an initial model and a prior model of the inversion conductivity of the inversion region; based on the initial model and the prior model of the inversion conductivity and the magnetotelluric observation data, constructing a mixed Lp regularization inversion objective function comprising an L1 regularization item, an L2 regularization item and a data fitting item; in an inversion iteration process, adaptively adjusting weight factors of the L2 regularization item and the L1 regularization item; and based on the inversion grid, performing magnetotelluric two-dimensional inversion according to the inversion objective function mixed with Lp regularization, the initial model and magnetotelluric observation data, and outputting optimal model parameters of Gaussian Newton inversion to obtain underground electrical structure information. According to the invention, the resolution and accuracy of the inversion result are improved.

Provided in the present application are a model training method, a defect detection method and related apparatuses. The embodiments of the present application can be applied to various scenarios such as computer vision. The model training method trains an initial detection model by means of using a first sampled image set containing some of images that have been used for training and a full newly-added second training image set, and thus, compared with using full historical training data and full newly-added training data to train an initial model, more saves time and reduces the GPU hour consumption; adaptive evaluation of corresponding first weights of model parameters restricts updating of the model parameters with respect to historical training data, so as to solve the problem of knowledge forgetting caused by only using full newly-added data for model fine-tuning, thus improving the learning capability of the detection model; and an optimized detection model obtained by using the model training method is used to detect defects in an image for detection, thus improving the effect and accuracy of defect recognition.

The invention relates to a power distribution network data intelligent analysis method based on data consanguinity and multi-modal fusion learning. The method comprises the following steps: S1, constructing a dynamically evolved data consanguinity topological graph; s2, designing a label-guided graph neural network architecture, embedding historical abnormal knowledge into a graph learning process, and outputting a deep semantic feature vector; s3, constructing a multi-modal fusion analysis framework, performing multi-dimensional feature fusion and data quality analysis, and identifying abnormal nodes; s4, designing a semi-supervised and incremental learning combined mixed training normal form, and performing model training and strategy optimization; and S5, based on the dynamic consanguinity topology constructed in the step S1 and the identified abnormal nodes, constructing a probabilistic reasoning framework, and fusing the model parameters obtained by optimization in the step S4 to realize quality abnormality root positioning and full-link visualization so as to form a complete data intelligent analysis scheme. According to the invention, efficient and accurate management of the topological data quality of the power distribution network is realized.

The invention discloses a large language model reasoning performance optimization method and device oriented to a resource-constrained environment, and the method comprises the steps: dynamically adjusting a calculation thread and memory resources of a large language model reasoning service based on the condition of available resources in the resource-constrained environment in a layer-by-layer reasoning process of the large language model reasoning service; model parameters stored in an SSD or a PM are gradually and asynchronously read and loaded into a memory by adopting an assembly line loading mechanism combined with the dynamic memory; and in the off-peak use period of the system, the memory space is released by actively identifying and deleting unimportant KV cache items for the cache KV Cache aiming at the key value stored in the memory and used for caching the model intermediate calculation result of the large language model. The method aims at solving the problems of memory limitation, non-uniform resource allocation and low reasoning efficiency when a large language model is efficiently deployed and executed on personal equipment, and the performance of the large model in a limited resource environment is optimized.

The invention discloses a tight reservoir three-dimensional crustal stress field modeling method based on an improved neural network. The tight reservoir three-dimensional crustal stress field modeling method comprises the steps that S1, a unified-format multi-source geological physical data tensor set is constructed; s2, constructing a frequency domain hierarchical enhancement-SIREN implicit neural network structure based on the unified format multi-source geological physical data tensor set; s3, inputting the candidate hyper-parameter configuration into the frequency domain hierarchical enhancement-SIREN implicit neural network to complete one-time model training; s4, aiming at each candidate hyper-parameter configuration, initializing an inner-layer population of the black widow optimization algorithm, completing second model training, and obtaining an optimal model parameter of the frequency domain hierarchical enhancement-SIREN implicit neural network; s5, tight reservoir fracturing parameter optimization and real-time safety window adjustment are achieved. According to the method, the continuous stress field can be quickly generated at the resolution of 1 m, real-time well section updating and fracturing scheme optimization are supported, and the fracturing transformation effect, the fracturing safety margin and the reliability of economic productivity prediction are remarkably improved in practical application.

The invention discloses a CAD automatic modeling method and system based on parameter driving. The method comprises the steps that model parameter information needing modeling is obtained; generating a CAD (Computer Aided Design) model on the basis of parameter analysis, geometric construction and surface recognition schemes; performing reverse reconstruction of the three-dimensional CAD model based on topology analysis, geometric feature extraction, pattern recognition and parameter relation inference; modeling, optimization and feedback evolution are carried out based on model analysis, feature learning and generation of parametric modeling rules; and according to the obtained data information, CAD automatic modeling based on parameter driving is completed. According to the method, CAD automatic modeling based on parameter driving is achieved, the reliability is higher, the accuracy is better, and the efficiency is higher.