42 results about "Neural Network Simulation" patented technology

The invention relates to the technical field of large model algorithms, in particular to a large model dynamic compression optimization method and system based on sparse pruning, and the method comprises the steps: capturing original weight fluctuation data generated by resource fluctuation in reasoning, and obtaining sparse weight reference data through sparse processing; analyzing calculation complexity through model reasoning delay data, and separating reasoning delay amount caused by a model scale; dynamically controlling the model compression ratio within a preset performance range based on the delay amount and the sparse reference data, and collecting reasoning precision distribution data under different compression parameters; evaluating a model performance state under each parameter by means of a neural network simulation method, and generating a performance state simulation result; determining a model quality optimization compensation parameter based on a simulation result by combining resource fluctuation data acquired in real time in a compression process; and finally, the compression strategy is adaptively regulated and controlled through the compensation parameters, and collaborative optimization of model calculation complexity, reasoning precision and delay during dynamic change of hardware resources is realized.

The invention discloses a three-dimensional magnetotelluric deep learning inversion method, and relates to the technical field of three-dimensional magnetotelluric inversion in electromagnetic exploration, and the method comprises the steps: constructing a three-dimensional layered underground resistivity theoretical model, and forming a sample pair through the structure data of the underground resistivity theoretical model and the corresponding visual parameter data; the method comprises the following steps: constructing a three-dimensional neural network based on a Swin Transform module and jump connection; a forward modeling sub-network is trained for the multiple visual parameters, and the weight of the forward modeling sub-network is frozen to serve as a fixed forward modeling operator, so that rapid forward modeling is achieved; after each fixed forward operator is migrated and spliced to the inversion sub-network, each fixed forward operator is used as an additional loss constraint term, and physical driving of neural network simulation is realized; and end-to-end mapping from each apparent parameter to the underground resistivity is established by fitting the inversion sub-network, and quasi-physics and data dual-drive three-dimensional magnetotelluric deep learning inversion is realized. The three-dimensional magnetotelluric inversion method has high practical value and popularization value in the technical field of three-dimensional magnetotelluric inversion with crossing of deep learning and electromagnetic exploration.

The invention relates to the technical field of intelligent site selection of new energy automobile charging facilities, and discloses an intelligent site selection base station management system and method, and the system comprises a city multi-dimensional data collection and preprocessing module, a knowledge base construction module, a city evolution dynamics simulation module, a future scene generation module, and a charging network optimization model construction module. A robust optimization solving module; the method comprises the following steps: collecting and constructing a knowledge base of multi-dimensional city data; simulating city dynamics by using multi-agent modeling and a space-time diagram neural network to generate a plurality of future scene sets; and finally, constructing a multi-target robust optimization model based on the scenes, and solving to generate a group of Pareto optimal robust charging network planning schemes. According to the method, the overall scientificity, foresight and robustness of charging base station site selection planning are improved, so that the investment decision risk of the charging base station site selection planning is effectively reduced.

Systems and methods are provided for analog hardware realization of neural networks. The method incudes obtaining a neural network topology and weights of a trained neural network. The method also includes transforming the neural network topology into an equivalent analog network of analog components. The method also includes computing a weight matrix for the equivalent analog network based on the weights of the trained neural network. Each element of the weight matrix represents a respective connection between analog components of the equivalent analog network. The method also includes generating a schematic model for implementing the equivalent analog network based on the weight matrix, including selecting component parameter values for the analog components.

The invention relates to the technical field of underground water monitoring, in particular to a high-precision underground water pollution plume migration prediction and source identification method, which comprises the steps of constructing a graph structure, simulating an underground water convection-dispersion process by using a space-time graph neural network, introducing a physical equation residual error constraint to train a prediction model, and converting source identification into an optimization problem. A multi-task strategy is adopted to synchronously identify a sparse source position and reconstruct a release history of the sparse source position, a model is finely adjusted and updated according to gradient descent cooperative solution, and uncertainty is quantified. According to the high-precision groundwater pollution plume migration prediction and source identification method, source position identification and release history reconstruction are synchronously realized through a multi-task strategy; organizing data by using a graph structure, constructing static and dynamic feature vectors, and comprehensively describing pollution migration space-time characteristics; a multi-stage curriculum learning, a splitting algorithm and a Bayesian optimization initialization strategy are adopted, gradient is calculated in combination with automatic differential, and a multi-source uncertainty decomposition framework is constructed to quantify uncertainty.

The invention discloses a deep learning-based precise sorghum fertilization amount dynamic adjustment method and system, and relates to the technical field of deep learning-based fertilization amount control. According to soil nutrient concentration and sorghum root physiological index data, a time sequence analysis method is adopted to generate a time sequence change sequence of root absorption capability; and inputting a pre-trained deep learning model to determine a nitrogen-phosphorus-potassium demand difference value. And when the demand difference value exceeds a preset demand threshold value, optimizing a fertilization amount ratio by using a convolutional neural network, simulating a response of a root system to fertilization adjustment in combination with a recurrent neural network, and outputting a target fertilization regulation and control scheme when it is detected that a predicted response accords with an expected yield index, so that equipment is controlled to perform fertilization subsequently according to the target fertilization scheme. According to the method, a nonlinear mapping relation between the fertilization amount and the physiological response of the sorghum root system is established through a deep learning technology, and the fertilization proportion of nitrogen, phosphorus and potassium is dynamically adjusted in combination with time sequence data, so that precise sorghum fertilization amount regulation is realized.

The invention discloses a nonlinear multi-agent system double-clock asynchronous hybrid neural adaptive control method, and relates to the technical field of nonlinear multi-agent system cooperative control. According to the method, a double-clock asynchronous framework is provided, time decoupling is carried out on updating of the leader observer and updating of the local observer, the limitation of synchronous updating of all assemblies is broken through, and the leader observer and the local assembly are made to operate independently; an event trigger pulse mechanism is designed, sampling is carried out only when a specific event occurs, the communication and calculation cost is remarkably reduced, the event trigger mechanism allows each agent to autonomously determine a trigger moment, and self-adaptive resource allocation between heterogeneous dynamic agents is achieved; and the numerical value of the nonlinear function is simulated and estimated by using the neural network, so that approximate state information can still be obtained under different conditions. And the weight of the neural network is only updated at the triggering moment, so that the resource consumption in the learning process is reduced.

Disclosed herein are related to a system, a method, and a non-transitory computer readable medium for simulating, predicting, or estimating, based on machine learning neural networks, wall stress of a body part. In one approach, a first neural network automatically detects features in multiple images of a body part. For example, the first neural network may detect, for each image, a lumen and a wall of an aorta. According to the detected features, a second neural network may simulate, estimate, or predict wall stress of the body part in response to pressure applied to the body part. For example, a model generator can generate a three-dimensional model of the body part according to the detected features in the multiple images, and the second neural network can simulate, estimate, or predict wall stress of the body part according to the three-dimensional model.

The application relates to a high-definition image explainable classification method and device, computer equipment and a storage medium. The method simulates the process of image perception, feature extraction, inductive reasoning and learning of the human brain. The method is based on the latent space of a pre-trained StyleGAN, a conversion network is designed to simulate the feature extraction of the visual cortex of the brain, effectively converting high-dimensional latent encoding into low-dimensional and decoupled classification features, and the problems of rule explosion and calculation collapse are alleviated; a fuzzy neural network is used to simulate the reasoning function of the parietal lobe and the prefrontal cortex of the brain, and an improved method of the fuzzy neural network structure suitable for the high-definition image explainable classification model is designed, so that the explainable classification of the low-dimensional features is realized; the fuzzy rules used for classification are visually displayed and analyzed, and a feature visualization method is designed based on the pre-trained StyleGAN generator, so that the explainability is further improved.

The application discloses an automatic needle insertion method combining a blood flow optimization model with a neural network. The method first calculates the blood vessel position and blood flow velocity distribution of a needle insertion area according to blood flow information measured by automatic ultrasonic scanning by means of the Doppler ultrasonic principle, then establishes a blood flow relative velocity optimization model and a neural network with a selection function, and decides the optimal needle insertion point and needle insertion angle on the basis of physical measurement results, and finally simulates the needle insertion path by means of a neural network with a learning function according to the needle insertion method of a professional. The method can be used for needle insertion of subcutaneous blood vessels of limbs and other body parts, and overcomes the difficulty of manual needle insertion when the blood vessel is not visible to the naked eye, thereby providing technical assistance for self-rescue and first aid in an environment lacking professional personnel.

The embodiment of the invention provides a distributed energy storage regulation and control method, device and system and a medium, and belongs to the technical field of electric power. The method comprises the steps of performing data processing on various random factors associated with distributed energy storage regulation and control based on multi-algorithm collaboration to generate a random scene for distributed energy storage regulation and control; processing battery operation parameters associated with distributed energy storage regulation based on a neural network model to estimate battery aging cost, the neural network model taking a battery capacity fading rate as an output; and determining a distributed energy storage regulation and control strategy based on the generated random scene and the estimated battery aging cost. According to the embodiment of the invention, scientific regulation and control and resource optimization configuration of the energy storage system are effectively realized, random factors and the battery aging cost of neural network simulation are comprehensively considered, and efficient application and accurate regulation and control of distributed energy storage in a power system are promoted.

The invention relates to the technical field of production management, and discloses a collaborative scheduling method, device and equipment for a digital manufacturing platform, and the method comprises the steps: generating a real-time sensing matrix through simulating the operation condition of a factory in real time, predicting material shortage information according to a real-time production work order and a material demand, obtaining the positioning data of an automatic carrying unit, and carrying out the real-time monitoring of the real-time sensing matrix; according to the method, a transport target map and a material carrying characteristic matrix are constructed, a graph neural network is utilized to simulate a transport path and optimize and drive an automatic carrying unit to carry out transport, risk assessment is carried out through multi-modal sensing data, an optimal adjustment strategy is generated, and through real-time data driving and path optimization, the precision and efficiency of production scheduling are improved, and the production efficiency is improved. The risk in the transportation process is reduced, the utilization rate of production resources is increased, and the problem that in the prior art, material demand changes are difficult to respond rapidly is solved.

The application belongs to the technical field of neural network simulation and analog, and particularly relates to a large-scale cortex pulse neural network asynchronous parallel simulation algorithm based on GPU. The application utilizes the advantages of multi-thread and texture memory of a computing graphics card, combines the general form of a biological brain receiving external stimulation and the general connection mode between neurons in the cortex, designs an asynchronous parallel algorithm framework of GPU and CPU, GPU is responsible for parallel evolution of neuron dynamics equations and block parallel calculation of isotropic connection in a local network, CPU is responsible for processing anisotropic long-range connection, and different neuron dynamics equations and plasticity learning rules can be compatible. Compared with the prior art, the application can effectively improve simulation speed, provides a tool for simulating a biological brain cortex in a single computing node, and is suitable for a single node multi-graphics card and a multi-node multi-graphics card distributed operation model.

This application provides a method for preparing a dry binder, achieving stable quality control through dynamic sensing and multi-level intelligent adjustment of the moisture content of the raw material powder: First, moisture content and environmental humidity data are collected in real time, and time series analysis is used to predict the trend of moisture content changes; then, support vector machine is used to classify seasonal and batch differences to determine the main influencing categories; for high humidity interference scenarios, neural network is used to simulate the dynamic impact of material residence time in the mixing equipment on moisture content and determine the risk level; based on the risk level, historical data is retrieved, and a random forest algorithm is used to generate a preliminary adjustment plan for process parameters; then, a digital simulation model is used to simulate and verify the entire process and iteratively optimize it to finally determine the optimal process parameters; during production, the moisture content prediction model is dynamically updated using real-time feedback data to achieve closed-loop correction and continuous optimization. This invention effectively reduces the quality risks caused by environmental fluctuations and batch differences.

This application belongs to the field of delay differential equation modeling, specifically disclosing a delay differential equation modeling method based on Bayesian optimization and neural networks. The method includes: generating multiple trajectory data of the system; searching for the optimal delay term using a Bayesian optimization algorithm, where the delay term to be solved is the optimization variable, and the error based on neural network simulation is the objective function, obtaining the optimal delay term through iterative optimization using a surrogate model; constructing a state matrix and a delay matrix from the trajectory data based on the optimal delay term, using the concatenated matrix as input to a neural network to construct a neural network for approximating a nonlinear function; integrating a linear multi-step method into the loss function of the neural network, training the neural network using the trajectory data to obtain a nonlinear function approximation model; and separating the numerical discretization error and the neural network approximation error based on this model to construct a total error bound. This application can achieve high-precision, high-efficiency delay differential equation modeling with quantization error guarantee.

The invention relates to the technical field of public safety management, in particular to a panoramic video fused public safety risk intelligent early warning method and system, and the method comprises the steps: obtaining panoramic video data of a target monitoring area in real time under a unified space-time framework; extracting spatial features and time sequence features from the acquired panoramic video data; fusing the spatial features and the time sequence features, and constructing a space-time diagram of the target monitoring area based on the fused spatial features and time sequence features; and simulating propagation and evolution of the risk in the space-time diagram by using a graph neural network, and carrying out risk decision making and early warning based on propagation and evolution characteristics.

The invention relates to the technical field of artificial intelligence decision analysis and behavior modeling, in particular to a figure decision mode analysis method and device based on a situation-reasoning one graph, and the method comprises the steps: constructing a decision mode situation-reasoning one graph, extracting and quantifying decision influence factors, and extracting and quantifying the decision influence factors. The method comprises the following steps of: firstly, converting mass unstructured open source data into a computable and deducible situation-reasoning graph by utilizing the structure of the situation-reasoning graph; then, according to a decision theory, quantifying intrinsic psychological cognition characteristics of a person, and taking the intrinsic psychological cognition characteristics as key constraint conditions of a reasoning network; and finally, constructing a decision-making mode neural network, and simulating a'perception stimulation-cognitive screening-logical reasoning 'decision-making process of a person under a specific situation. According to the method, deep fusion and accurate analysis from an external objective situation to internal subjective cognition are realized.

This invention relates to the field of large model algorithm technology, and in particular to a dynamic compression optimization method and system for large models based on sparse pruning. The method captures raw weight fluctuation data caused by resource fluctuations during inference, and obtains sparse weight baseline data through sparsification. It analyzes the computational complexity of model inference latency data to separate the inference latency caused by model size. Based on the latency and sparse baseline data, the model compression ratio is dynamically controlled within a preset performance range, and inference accuracy distribution data under different compression parameters are collected. The model performance status under each parameter is evaluated using neural network simulation methods, generating performance status simulation results. Combined with real-time resource fluctuation data acquired during compression, model quality optimization compensation parameters are determined based on the simulation results. Finally, the compression strategy is adaptively adjusted through the compensation parameters to achieve coordinated optimization of model computational complexity, inference accuracy, and latency when hardware resources change dynamically.

To provide methods, systems and recording mediums for generating suggested communications during a multi-agent interaction using a language model neural network.SOLUTION: A method comprises: identifying multiple first and second strategies for selecting between parameterizations for communications for first and second actors; parametrizing a set of context variables for the communications of the actors during an interaction; for each of multiple pairs including respective first and second strategies: generating simulations of the interaction using a language model neural network; determining, from the simulations, a score for the pair that indicates a degree to which the first and second actors satisfy objectives for the interaction; selecting, using the scores for the multiple pairs, a parametrization for the first actor; and generating, by processing an input that conditions the language model neural network on the selected parameterization, a suggested communication for the first actor.SELECTED DRAWING: Figure 2

An automated insulin delivery system provides an insulin injection that is computed by adding (i) the recommendation of an artificial neural network trained to mimic a constrained model predictive controller dosing rule from a neural network implementing an artificial pancreas (ii) a hypoglycemia mitigation system includeds a correction (one dose computed as the correction down to 110mg / dl based on prevailing continuous glucose monitoring and once an hour at most, unless there is a triggering BPS and G>180 mg / dl), and (iii) the current basal rate (output of the Performance Assessment System, (PAS)); that amount is then saturated by the Safety Supervision System, SSM. Finally, a priming bolus from the Bolus Priming System, BPS, is added to the total if the conditions for large glycemic excursions are detected, also saturated by SSM.

The invention discloses a carbonate rock acid fracturing effect main control parameter analysis method and system, relates to the technical field of acidification, and solves the problem that main control parameters are difficult to identify in existing carbonate rock acid fracturing effect assessment. Calculating the correlation of each parameter based on the parameter data, and filtering out the parameters with extremely high correlation; executing a random forest algorithm and a recursive elimination algorithm based on the parameters to obtain a first weight and a second weight of the parameters; performing weighted average on the first weight and the second weight to obtain a comprehensive weight of the parameter; selecting parameters according to the comprehensive weight to construct a plurality of schemes, inputting the schemes into a BP neural network to simulate the yield to obtain the correlation between the actual yield and the predicted yield, and selecting the parameters contained in the scheme with the highest correlation as main control parameters of the acid fracturing effect; and analyzing carbonate rock acid fracturing effect master control parameters based on a random forest, recursive elimination and a BP neural network algorithm.

The invention provides a universal satellite remote sensing atmospheric roof radiation simulation method, which belongs to the technical field of remote sensing image data processing, and comprises the following steps: (1) calculating latitude and longitude and observation geometric information of a transit area; (2) constructing global environment parameter background field data; (3) respectively performing radiation simulation on the ocean area and the land area in the transit area; (4) respectively calculating the atmospheric top radiance of the ocean area and the atmospheric top radiance of the land area; and (5) splicing the atmospheric top radiance data of the ocean area and the atmospheric top radiance data of the land area to obtain the atmospheric top radiance data of the whole image. According to the universal satellite remote sensing atmosphere top radiation data simulation method, the reliability of a physical model and the modeling capacity of a neural network are combined, the problem that existing satellite remote sensing atmosphere top radiation simulation is low in reliability is solved, and compared with full-data neural network simulation, consumed time is shorter.

The application discloses a boundary level grid physical constraint variational assimilation method embedded in a deep neural network, comprising: establishing a momentum equation containing a boundary level grid turbulent friction term, wherein the boundary layer turbulent friction term is simulated through a deep neural network; and constructing a weak constraint term of a variational assimilation framework cost function with the momentum equation; training the deep neural network with a dataset constructed from historical numerical weather prediction model simulation results; linearizing the trained deep neural network to obtain a corresponding tangent linear operator and an adjoint operator, and embedding the tangent linear operator and the adjoint operator into the variational assimilation framework cost function; obtaining multi-source remote sensing observation data and a numerical weather prediction model background field, and solving an analysis field by taking minimization of the cost function as an objective, to complete data assimilation. The application can improve the data assimilation and numerical prediction level of disastrous weather such as typhoon, especially the observation assimilation level related to the boundary layer.

The invention discloses a boundary hierarchical grid physical constraint variation assimilation method embedded with a deep neural network, which comprises the following steps: establishing a momentum equation containing a boundary hierarchical grid turbulence friction item which is simulated through the deep neural network; constructing a weak constraint term of a variational assimilation framework cost function by using the momentum equation; training the deep neural network by using a data set constructed by a historical numerical weather forecast mode simulation result; linearizing the trained deep neural network to obtain a corresponding tangent linear operator and an adjoint operator, and embedding the tangent linear operator and the adjoint operator into the variational assimilation framework cost function; and acquiring multi-source remote sensing observation data and a numerical weather forecast mode background field, and solving an analysis field by taking the cost function minimization as a target to complete data assimilation. According to the method, the data assimilation and numerical forecasting level of disastrous weather such as typhoon can be improved, and particularly the observation assimilation level related to a boundary layer can be improved.

An automated insulin delivery system provides an insulin injection that is computed by adding (i) the recommendation of an artificial neural network trained to mimic a constrained model predictive controller dosing rule from a neural network implementing an artificial pancreas (ii) a hypoglycemia mitigation system includeds a correction (one dose computed as the correction down to 110mg / dl based on prevailing continuous glucose monitoring and once an hour at most, unless there is a triggering BPS and G>180 mg / dl), and (iii) the current basal rate (output of the Performance Assessment System, (PAS)); that amount is then saturated by the Safety Supervision System, SSM. Finally, a priming bolus from the Bolus Priming System, BPS, is added to the total if the conditions for large glycemic excursions are detected, also saturated by SSM.

This invention proposes a general method for simulating atmospheric top radiation from satellite remote sensing, belonging to the field of remote sensing image data processing technology. The method includes: (1) calculating the latitude, longitude, and observation geometry of the transit area; (2) constructing global environmental parameter background field data; (3) performing radiation simulations for the ocean and land regions within the transit area; (4) calculating the atmospheric top radiance of the ocean and land regions respectively; and (5) stitching together the atmospheric top radiance data of the ocean and land regions to obtain the atmospheric top radiance data for the entire image. This general method for simulating atmospheric top radiation from satellite remote sensing combines the reliability of physical models with the modeling capabilities of neural networks. It addresses the low reliability of existing satellite remote sensing atmospheric top radiation simulations while also being less time-consuming compared to full-data neural network simulations.

The invention discloses a mine filling body spontaneous combustion trend analysis method based on rapid identification. The method comprises the following steps: acquiring field sample data of a mine filling body material, processing the sample data, extracting a mineral proportion, acquiring component distribution information, simulating an oxidation reaction path among different minerals by adopting a neural network, and determining an interaction strength value; based on the interaction strength values, sulfide and organic matter proportion subsets are obtained, high-risk interaction combinations are calibrated through logic screening rules, and risk classification labels are obtained; the method comprises the following steps: extracting an inflammable characteristic parameter sequence through historical spontaneous combustion data, obtaining a temperature and gas concentration correlation sub-sequence from the inflammable characteristic parameter sequence, and optimizing the sub-sequence to obtain a dynamic change trend index; according to the dynamic change trend index, the spontaneous combustion trend type is judged, an early warning level label is obtained, mine site environment parameter integration is obtained through the early warning level label, and a final spontaneous combustion trend prediction result is determined. According to the invention, the accuracy and real-time performance of spontaneous combustion risk prediction are improved.

The present application relates to the technical field of artificial intelligence decision analysis and behavior modeling, in particular to a kind of situation-reasoning one figure-based character decision mode analysis method and equipment, including the construction decision mode situation-reasoning one figure, the extraction and quantization of decision influencing factors and the extraction and quantization steps of decision influencing factors, in the execution above-mentioned steps, first, the structure of situation-reasoning one figure is used, and massive unstructured open source data is converted into computable, situation-reasoning one figure that can be deduced;Then, according to the decision theory, the internal psychological cognitive characteristics of the character are quantified, and they are used as the key constraint condition of reasoning network;Finally, the decision mode neural network is constructed, and the decision process of "perception stimulation-cognitive screening-logical reasoning" of the character under a certain situation is simulated.The present application realizes the deep integration and accurate analysis from external objective situation to internal subjective cognition.

This application relates to the field of product carbon footprint calculation, and discloses a method and system for constructing a product carbon footprint model based on intelligent agents. This method automatically collects and standardizes product information from multiple data sources using a large language model, generates product production process flows and identifies emission sources through a lifecycle model inference module, simulates activity data using deep neural networks, and matches emission factors in a background database using semantic similarity. Finally, a carbon footprint calculation model is constructed based on the ISO 14067 standard. This method overcomes the problems of difficult data acquisition and time-consuming processes in traditional carbon footprint calculation methods, achieving intelligent construction of product carbon footprint models and improving the efficiency and accuracy of carbon footprint calculation.