755 results about "Analogue computation" patented technology

The invention discloses a turbofan engine operation monitoring method and system based on digital twinning, belongs to the technical field of turbofan engine monitoring, and aims to solve the problems that weak fault signals such as early cracks and abrasion are difficult to extract and the prediction precision of a multi-source fault propagation path is low under a strong noise background. An original operation signal is collected through a sensing array, and is processed by an adaptive resonance demodulation chain to generate a demodulation signal. The method comprises the following steps: carrying out time-frequency transformation on a demodulation signal, constructing an initial candidate feature set by combining feature frequency prior matching actual measurement and theoretical feature frequency, and generating an independent feature set by fusing multi-scale decoupling network separation features of digital twin constraints; for independent features, effective causal pairs are screened by adopting a physical coupling relationship combining Granger causal analysis and digital twinborn simulation, a dynamic Bayesian network is constructed to simulate fault propagation, a posterior probability is calculated through digital twinborn verification and Monte Carlo simulation, early warning is triggered, and a maintenance decision is generated. And weak signal extraction and accurate fault prediction under strong noise are realized.

The invention belongs to the technical field of multi-physics field coupling simulation, and particularly relates to a multi-physics field coupling Fluent simulation analysis integrated system, which comprises a data preprocessing module used for carrying out data integration, grid division and boundary condition setting on a multi-physics field coupling problem; the multi-physical field coupling module comprises a fluid-structure coupling sub-module, a thermal-force coupling sub-module, an electromagnetic-fluid coupling sub-module and the like, and interaction simulation among different physical fields is realized respectively; the calculating and solving module is used for calculating and solving a multi-physics field coupling problem based on a Fluent software platform and coordinating the calculating sequence and the time step length of each physics field; and the post-processing and visualization module is used for performing post-processing analysis and visualization display on the simulation result. The method effectively solves the problems that a traditional system is low in data interaction efficiency, insufficient in grid precision and poor in calculation coordination, and has the advantages that simulation precision is improved, resource consumption is reduced, and system expansibility is enhanced.

The invention relates to the technical field of big data, in particular to an intelligent operation and maintenance management method based on a big data algorithm, and the method comprises the steps: constructing and continuously updating a dynamic fault association graph through inputting multi-source heterogeneous operation and maintenance data; starting full-graph scanning based on a predefined period, detecting an abnormal topological structure through a graph pattern recognition algorithm, and marking potential risk nodes; executing dynamic influence diffusion simulation on the potential risk nodes, calculating a business influence severity quantized value after the fault, and marking fault propagation vulnerabilities according to the quantized value; taking the potential risk node as a starting point, executing a reverse traceability algorithm for preferentially exploring a path pointing to a fault propagation vulnerable point, and outputting a fault propagation path and a source fault node identifier; and finally generating and executing a fault processing strategy. The process solves the problem that traditional operation and maintenance cannot quantitatively evaluate and discriminate the highest priority disposal object from numerous potential risks, and realizes accurate positioning and active prevention and control of weak links of fault propagation.

The invention relates to the technical field of oil exploitation, and provides a CFD-based electric submersible pump oil-gas-water three-phase flow adaptability analysis method and system, and the method comprises the steps: constructing a three-dimensional geometric model of an electric submersible pump, carrying out the initial grid division through CFD software, and optimizing the grid density through a self-adaptive grid refinement algorithm; setting oil well boundary conditions, a turbulence model and cavitation model parameters; steady-state and unsteady-state simulation calculation is carried out, and a flow field distribution diagram in the electric submersible pump is obtained; key performance parameters are extracted; simulating flow characteristics of oil-gas-water three-phase flow by adopting a turbulence model optimization algorithm based on multiphase flow coupling and combining a turbulence model, a multiphase flow model, an interphase turbulence correction term and an interphase interaction force model; and carrying out adaptability analysis on the oil-gas-water three-phase flow of the electric submersible pump. All-around technical support can be provided for design, operation and maintenance of the electric submersible pump, and technical progress and development of the oil exploitation industry are powerfully promoted.

The invention provides a method and a system for measuring plasma current in a quasi-ring symmetrical star simulator, and relates to the technical field of star simulators, and the method comprises the steps: determining a sensor design index according to a bootstrap current simulation calculation result, and converting the design index into an actual arrangement scheme according to an installation environment constraint; all the magnet coils are independently excited in a plasma-free state, wall eddy current interference signals generated by the magnet coils are calibrated, and thus wall eddy current calibration voltage signals are output; a sensor in an actual arrangement scheme is used for synchronously collecting a total original induced voltage signal including plasma current and wall eddy current; an integral voltage signal representing the total current is output; and compensating the integral voltage signal by using the wall eddy current calibration voltage signal, and inverting the final measurement value of the plasma current in the quasi-ring symmetric star simulator. The device and the method have the beneficial effects that the interference of a stray field on a measurement result is weakened, and a high-precision diagnostic tool is provided for the plasma physical research of the star simulator.

The invention provides an accident consequence simulation calculation method based on mathematical physical model coupling solution, and the method comprises the steps: constructing an accident chain knowledge graph and a physical trigger graph, and building a causal relationship and physical constraints among equipment, states, events and consequences; gathering and mapping historical records, expert rules and online observation data into map entities and relationships, forming a baseline accident scene and calculating a baseline index; generating candidate paths by utilizing graph reasoning and coupled multi-physics field simulation, and realizing a closed loop of graph reasoning and physical solution through consistency check; performing scoring and disturbance simulation analysis on the candidate paths, and screening robust target paths; and carrying out high-fidelity simulation on the target path, identifying key nodes in combination with sensitivity analysis and a minimum cut-off set, and generating disposal suggestions and action priorities. According to the method, high-credibility prediction of accident evolution and emergency response closed-loop linkage are realized, and the method has high precision, high robustness and engineering implementability.

The invention discloses a bay environment simulation forecasting method and device, relates to the technical field of marine environment forecasting, and aims to solve the problem that in the prior art, forecasting precision and forecasting timeliness cannot be taken into account when environment simulation forecasting is carried out on a bay. The method comprises the following steps: based on coastline data, performing encryption processing on a target sub-region in a target gulf region by adopting a curve orthogonal grid, and generating a variable-resolution refined curve orthogonal grid from an inshore region to an open sea region; based on a refined curve orthogonal grid, according to a model simulation data format requirement, a variable-resolution grid file is manufactured and obtained; determining a model input file based on the water depth topographic data and the variable resolution grid file; and inputting the model input file into a preset three-dimensional oblique pressure ocean numerical model for calculation to obtain simulation forecast data. Therefore, the simulation precision requirements of different bay areas can be met, and the simulation calculation speed and the simulation calculation efficiency of the whole target bay area are ensured.

The invention relates to the technical field of neuroscience and medical treatment, in particular to an individual head model dynamic optimization method and system based on transcranial alternating current stimulation. According to the method, the three-dimensional initial individual head model is partitioned, the parameters are set, the real-time electrical stimulation parameters and the real-time electroencephalogram information of the individual head under the stimulation effect are synchronously obtained, and then the parameters of the initial head model are adjusted according to the real-time data; then, according to the obtained data, simulating and calculating electrophysiological change data of the three-dimensional initial individual head model under the action of initial transcranial alternating current stimulation, and based on a focusing index value and a uniformity index value calculated through the electrophysiological change data, optimizing dynamically-adjusted head model parameters, and finally, calculating a focus index value and a uniformity index value of the three-dimensional initial individual head model under the action of initial transcranial alternating current stimulation. The information entropy difference value obtained through calculation of the optimized head model parameters and the real-time electroencephalogram information is compared with a preset difference threshold value, a clear termination condition is provided for optimization of the head model parameters, and therefore dynamic optimization of the individual head model is achieved.

The invention relates to the technical field of image processing, and provides an optimization method and device for a spectrum processing chip, electronic equipment and a storage medium, and the method comprises the steps: constructing a target function for designing modulation units, and enabling the target function to represent a mapping relation between the structure parameters of the micro-nano structure of each modulation unit and the demands of a target application scene; performing optimization solution on the target function based on the natural spectral image sample set in the target application scene, and determining structure parameters of the micro-nano structure of the modulation unit; and constructing the modulation unit according to the determined structural parameters of the micro-nano structure of the modulation unit, and generating the spectrum processing chip according to the constructed modulation unit and the image sensor. According to the invention, the spectrum processing chip is constructed, so that the plurality of modulation units and the image sensor in the chip jointly carry out optical simulation calculation of the inner product of the feature vectors on the incident natural spectrum image, visual light information of a spatial domain and a frequency domain can be directly detected, acquired and perceived in real time, and subsequent analysis of the feature information is efficient and rapid.

The invention relates to the field of multiphase numerical simulation, and discloses a numerical simulation method for solid and liquid phases in a rotating flow channel based on multiple reference systems, which comprises the following steps of: firstly, acquiring a rotating flow field generated by a multiple reference system method, and extracting rotating domain information as well as particle information and fluid computational domain grid information in a current time step; secondly, particles in a rotation domain and particles outside the rotation domain are determined according to the rotation domain information and the particle information; in combination with the particle information and the fluid calculation domain grid information, the final flow field speed and the reconstructed porosity of the particles in the rotation domain and the particles outside the rotation domain are calculated respectively, and then the drag force is calculated according to the final flow field speed, the reconstructed porosity and the particle information; meanwhile, according to the particle information and the rotation domain information, the Korotkoff force and the centrifugal force of the particles in the rotation domain are calculated. Finally, drag force, Korotkoff force and centrifugal force are calculated through iteration, particle information and grid information of the next time step are obtained, and therefore numerical simulation calculation of particle motion in the rotating flow channel under the fixed grid is achieved.

The invention discloses a low-sample neural network structure reliability evaluation system and evaluation method, and relates to the technical field of engineering structure safety monitoring, and the evaluation system comprises a cloud server which is used for constructing a recurrent neural network model containing a static variable embedding mechanism, completing model training and converting a model format; the edge calculation terminal is used for receiving and preprocessing real-time data of the sensor, executing multi-step prediction to output a future time period response sequence, and calculating a future failure probability through virtual Monte Carlo simulation; the sensor assembly is used for collecting structure state time sequence data; and the communication module is used for realizing data interaction and alarm signal transmission operation. According to the method, collaborative modeling of time-varying and static uncertainty is realized by adopting a static variable embedded recurrent neural network model, failure probability distribution is generated at an edge computing terminal in combination with a virtual Monte Carlo technology, failure risk prediction in a future time period is supported, and real-time and accurate reliability early warning can be realized in a resource limited scene.

The invention provides a numerical simulation and stability analysis method and system based on monitoring data, and belongs to the technical field of monitoring and early warning. The problems that in the slope monitoring technology, data collecting and processing efficiency is low, calculation model precision and adaptability are poor, dynamic monitoring of slope stability lacks pertinence, the system integration degree is low, cost is high, and application is complex are solved. The method comprises the steps that slope data are collected in real time through a multifunctional sensor; transmitting the acquired data to a data acquisition system for real-time data monitoring and recording; a two-dimensional numerical model of the side slope is constructed through finite element analysis software, and the side slope is analyzed through the two-dimensional numerical model; and comparing and analyzing real-time monitoring data in the data acquisition system and a numerical simulation calculation result, analyzing the change trend of the side slope, identifying a potential instability area, updating the two-dimensional numerical model in real time by using an automatic algorithm, completing real-time dynamic evaluation of the side slope, and early warning potential side slope instability risks in advance.

The invention discloses a concrete structure seismic damage assessment method and system based on multi-source data feature fusion, belongs to the technical field of civil engineering structure seismic damage assessment, and aims to solve the problem that single-source data cannot accurately reflect the seismic damage state of a concrete structure. Extracting geometric deformation characteristics by using a laser radar, and extracting overall rigidity characteristics by using an acceleration sensor; holographic damage feature vectors are constructed through a weighted fusion algorithm, constitutive parameters of the nonlinear finite element model are automatically corrected through the vectors, and finally the residual bearing capacity of the structure is obtained through numerical simulation calculation. Objective and rapid recognition of post-earthquake structural damage is realized, and a specific structural safety reserve quantitative index can be output.

The invention relates to the technical field of robots, in particular to a complicated scene-oriented intelligent robot perception decision-making method and system. The method comprises the following steps: acquiring multi-modal environment sensing data; generating a warehouse point cloud map based on the multi-modal sensing data; marking shelf positions by using a warehouse point cloud map; when the intelligent robot with the body is driven to advance to the goods shelf position, goods posture information is collected, and goods surface features are recognized; cargo grabbing points are selected based on cargo surface features, and the grabbing posture of a mechanical arm is planned; a grabbing track is generated according to the grabbing posture of the mechanical arm; executing cargo grabbing simulation according to the grabbing track, and calculating a cargo slipping risk; and when the goods sliding risk is higher than a preset risk threshold value, the grabbing posture of the mechanical arm is adjusted, and mechanical arm grabbing optimization data is generated. According to the method, the success rate and the execution efficiency of the grabbing and placing tasks of the intelligent robot with the body in a complex scene are improved based on the robot technology.

The invention relates to the technical field of plasma numerical simulation, in particular to a data processing system suitable for a two-dimensional boundary plasma fluid calculation program SOLPS. According to the technical scheme, the system comprises a simulation calculation convergence state analysis module, an experiment and simulation comparison module and a simulation result processing module. According to the method, one-key data processing is realized through an automatic script and a graphical interface, and the limitation of a specific computing environment is broken through; the system can quantitatively analyze the simulation convergence state, provides a direct comparison function of experiment and simulation data, and provides a reliable basis for transport coefficient calibration. Meanwhile, flexible customization of various visualization forms is supported, the problems that a traditional method is tedious in operation, low in efficiency and insufficient in flexibility are effectively solved, and the efficiency, accuracy and reliability of data processing are comprehensively improved.

The invention relates to the technical field of thermal protection of hypersonic aircrafts, in particular to a multi-scale prediction method for an ablation behavior of a thermal protection structure based on data driving, which comprises the following steps: performing molecular dynamics simulation on a gas-solid interface of an ablation type thermal protection material to obtain simulation calculation activation energy; obtaining the experimental activation energy of the material; performing Bayesian optimization on the simulation calculation activation energy and the experiment measured activation energy to obtain optimized activation energy; performing macroscopic wall surface ablation simulation by taking the optimized activation energy as a boundary condition to obtain an ablation parameter database recording ablation wall surface temperatures, ablation rates and ablation depths corresponding to different flow rates, altitudes and time; training a radial basis function neural network based on the ablation parameter database, and establishing an ablation prediction model; obtaining a prediction result; the calculation efficiency and accuracy of the thermal protection structure ablation behavior multi-scale prediction method can be improved.

The invention discloses a design method of a star simulator three-dimensional divertor target plate, and belongs to the technical field of magnetic confinement fusion. The method comprises the following steps: firstly, deducing a circumferential thermal load uniformity theoretical model, then, calculating background information required by target plate generation, and finally, carrying out target plate generation by using the obtained background information and the circumferential thermal load uniformity theoretical model. The target plate is generated based on parameter control, the target plate capable of achieving uniform distribution of the circumferential thermal load is provided, the circumferential uniform distribution of the thermal load of the divertor target plate can be effectively improved, and the peak value of the thermal load of the divertor target plate can be effectively reduced. The target plate generated by the method conforms to standard output formats among different analog calculations. Data can be conveniently transmitted between integrated simulation platforms, and the working process is simplified. The divertor target plate is generated on the basis of the basic physical assumption that the magnetic lines of the magnetic field and the heat flow are attenuated along the radial position, and the method is suitable for all star simulator devices with three-dimensional magnetic island structures and has universal applicability.

The invention discloses a flood forecasting method and device based on multi-model cooperation and a medium, belongs to the technical field of flood forecasting and flood disaster simulation, and is used for solving the technical problems of insufficient coupling of flood forecasting models, weak visualization ability, lack of real-time interaction and non-visual information transmission in a traditional flood forecasting method. The method comprises the steps of collecting water conservancy information data of a target drainage basin; according to drainage basin characteristics of the water conservancy information data, collaborative architecture interaction under related multiple models is carried out on water conservancy factors in the target drainage basin, and a water outlet professional collaborative model is constructed; through a preset UE5 engine, performing three-dimensional modeling simulation calculation related to a flood routing process and a flood inundation range on result data output by the water professional cooperation model to obtain a three-dimensional simulation scene; based on the three-dimensional simulation scene, performing multi-dimensional information broadcasting on the real-time water conservancy data, and determining multi-dimensional simulation forecast data; and performing key index verification analysis on the multi-dimensional simulation forecast data to obtain flood control decision information.

The invention relates to the technical field of organic light-emitting device production process data processing, and provides an organic light-emitting device production process optimization method, which comprises the following steps of: monitoring key parameters in an evaporation cavity in real time, analyzing data in a cleaning treatment process by utilizing a preset statistical algorithm, and optimizing the production process of an organic light-emitting device. And extracting feature data influencing the surface cleanliness and flatness of the substrate. And then, establishing a mathematical model in the vacuum evaporation process, and solving by applying an optimization algorithm to obtain a parameter combination which enables the performance of the device to be optimal. Furthermore, electrical and optical performance and life stability data of the device are collected, and optimization parameters are simulated, calculated and optimized through a data processing model. In the production process, equipment operation data are collected in real time, optimization parameters and real-time data are compared, and the parameters are optimized in real time through a stochastic gradient descent method and stored in a knowledge base. The device performance and the production efficiency are effectively improved, and the production cost is reduced.

The invention discloses a method for efficiently and accurately simulating three-dimensional electron diffraction dynamics intensity, and aims to solve the problems of low simulation calculation efficiency and insufficient precision in the prior art. According to the method, a reciprocal vector is rotated by adopting a Rodrigues formula through optimizing a reciprocal space sampling strategy, and a user-defined phase difference or random sampling is introduced in two dimensions of rotation and precession, so that the sampling frequency is remarkably reduced. And meanwhile, the diffraction intensity is accurately calculated by utilizing a Bloch wave method, and the diffraction intensity of the chiral crystal and the enantiomer thereof is calculated at one time by setting the negative thickness, so that repeated calculation is avoided. The invention supports a plurality of diffraction means, including electron diffraction automatic tomography (ADT), rotating electron diffraction (RED), precession electron diffraction tomography (PEDT), continuous rotating electron diffraction (cRED) and continuous rotating precession electron diffraction tomography (cPEDT), and can achieve convergence within 50, 100 and 500 times of sampling of single diffraction. According to the method, the calculation efficiency is improved, the application range of simulation is widened, and reliable technical support is provided for material structure analysis.

The invention provides a numerical-digital accurate prediction method and system for artificial stratum freezing multi-physics field evolution. The method comprises the following steps: S1, constructing a numerical model of artificial stratum freezing engineering multi-physics fields; s2, a sensor collects multi-physical field data of an engineering field in real time and transmits the multi-physical field data to a data transmission server in real time; s3, carrying out simulation calculation on the numerical model, extracting a multi-physics field calculation result of the numerical model at the measurement position of the sensor at preset time, and comparing the multi-physics field calculation result with the multi-physics field data of the engineering site at the corresponding time; s4, judging whether the difference value between the multi-physics field calculation result and the multi-physics field data is smaller than an allowable value or not, if so, entering S5, and if not, adjusting parameters by an optimization algorithm, and returning to S3; s5, obtaining an optimized numerical model; s6, repeatedly executing the steps S3-S5 in a project proceeding process, and continuously updating and optimizing the numerical model; and S7, a result is extracted from the updated optimization numerical model, and the artificial stratum freezing multi-physics field evolution process is accurately predicted.

The invention discloses a quantum cognitive simulation system and method based on classical computation, and belongs to the technical field of quantum computation.A data processing module receives original data of external data input equipment, converts the original data into quantum state data after processing, and transmits the quantum state data to a quantum cognitive simulation computation module; the quantum cognitive simulation calculation module performs quantum cognitive simulation calculation by using a CPU cluster, a GPU array and a coprocessor, and stores a result in the storage module or directly transmits the result to the output module; and the output module performs format conversion on the calculation result and outputs the result to external display / control equipment. The quantum cognitive simulation system and method based on classical calculation aim at overcoming the defects of a traditional cognitive model in the aspects of processing decision fuzziness, context dependency and high-dimensional calculation efficiency, the effects of quantum superposition, interference, entanglement and the like can be efficiently simulated, quantitative calculation of related functions of human consciousness is achieved, and the quantum cognitive simulation system and method based on classical calculation can be applied to the field of quantum computing. And the performance in an actual application scene is optimized.

The invention discloses a processing process fault prediction method for essential oil production, and relates to the technical field of essential oil processing, and the method comprises the steps: constructing an environment equipment coupling model, and drawing an electrical equipment insulation resistance attenuation curve through a salt mist deposition experiment; environment disturbance variables are introduced into the generative adversarial network, and an anomaly detector capable of recognizing salt spray corrosion characteristics and humidity condensation characteristics is trained; capturing time-space correlation between temperature mutation and quality fluctuation through a self-attention mechanism; calculating a probability density function of the residual service life of the equipment in real time, and automatically correcting a temperature over-limit threshold value based on the residual service life distribution; calculating the contribution degree of each node to the fault through intervention simulation; and constructing a digital twinborn model containing thermodynamic and hydrodynamic characteristics. Through environment dynamic modeling, causal reasoning and digital twinning methods, the key problems of low fault prediction precision, poor traceability efficiency and lack of quantitative support in decision making in essential oil production are solved.

The invention relates to the technical field of industrial digitization and carbon monitoring, in particular to an industrial park carbon emission dynamic monitoring and evaluation system based on digital twinning, which comprises a digital twinning construction module, a carbon emission analog calculation module, a monitoring task configuration module, a data enhancement processing module, a situation fusion generation module and a dynamic evaluation output module. Data are collected through a sensor network to drive digital twinborn evolution, carbon emission space-time distribution is simulated, a hot spot area is identified, a monitoring task of the sensor network is dynamically configured accordingly, and self-adaptive focusing of monitoring resources is achieved. And the system executes the task set to obtain high-resolution data, after enhancement processing, the high-resolution data and a simulation result are subjected to cross-space-time fusion, a carbon emission situation map is generated, and the carbon emission intensity and the change track are evaluated according to the carbon emission situation map. According to the system, dynamic optimization of a monitoring strategy and deep fusion of virtual and real data are realized, and the accurate efficiency of monitoring and the comprehensive accuracy of evaluation are improved.

The invention belongs to the technical field of dike breach emergency rescue, and discloses a dike breach breach potential intelligent deduction and submerging probability visualization system and method and a medium. The engineering intervention deduction engine is constructed on the basis of a physically guided neural operator, takes a two-dimensional multi-physical reconstruction field containing pile body positions, material distribution, a flow velocity field and terrains as input, and directly outputs a water flow velocity-water depth physical field time history in a future time period; the online self-evolution engine is used for triggering fine adjustment of the model by comparing a deduction result with the deviation of real-time monitoring data; and the uncertainty quantification visual interface is used for dynamically displaying the deduction result in the form of a probabilistic submerging thermodynamic diagram. According to the method, a deduction framework integrating physical constraints and neural operators is created for the first time, the technical problems that traditional numerical simulation calculation is low in efficiency and pure data driving methods are physically inconsistent are solved, the unification of minute-level deduction speed and physical rule following is achieved, and real-time and reliable scientific decision support is provided for dike breach emergency rescue.

The invention provides a computing device and a computing chip, and belongs to the technical field of computers. The computing device can comprise a digital-to-analog conversion module, an analog computing module and an analog-to-digital conversion module. The digital-to-analog conversion module can be used for converting the mantissa part of the input floating-point number into an initial analog signal, and performing gain adjustment on the initial analog signal based on the exponent part of the input floating-point number to obtain an input analog signal; the analog calculation module can be used for performing analog calculation based on the input analog signal to obtain a calculation result analog signal; the analog-to-digital conversion module can be used for converting the calculation result analog signal into a result floating-point number. According to the calculation device, the input floating-point number is converted into the input analog signal, floating-point number calculation is carried out in an analog domain, and compared with floating-point number calculation in a digital domain, the calculation efficiency of floating-point calculation can be improved.

The invention relates to a full-simulation non-multiplication and addition operation method and device for simulation in-memory calculation, and the method comprises the steps: training a target full model, and obtaining a corresponding weight and a batch normalization parameter, the batch normalization parameter comprising bias information and a zoom factor; based on the simulation in-memory calculation array and the simulation bias array, in combination with the weight and bias information, carrying out multiplication and addition operation and batch normalized bias calculation; generating a target reference voltage of the ReLU-ADC based on a global resistance ladder voltage divider or an analog bias array in combination with a scaling factor, and setting a DAC minimum output voltage thereof; and based on the target reference voltage and the minimum output voltage of the DAC, the ReLU-ADC performs full-simulation non-multiplication-addition operation on the bias multiplication-addition simulation calculation result. Therefore, the problems that in a traditional CIM architecture, a mode of completing non-multiplication and addition operation depending on a digital domain has significant bottlenecks in key performance indexes such as speed, power consumption and area, and the requirements of resource-limited scenes such as an intelligent sensor are difficult to meet are solved.

The invention relates to an intelligent fire extinguishing method, system and equipment for a fire-fighting unmanned aerial vehicle and a medium. The method comprises the following steps: acquiring an original depth image and an original infrared image, extracting geometric and thermal feature vectors based on the original depth image and the original infrared image, and generating a dynamic fire source attention map; generating an enhanced environment state vector by fusing the dynamic fire source attention map with the multi-modal features, inputting the vector and the historical state memory at the current moment into a time sequence decision model for time sequence updating to obtain updated historical state memory, inputting the updated historical state memory into a strategy generation model, and outputting a continuous action instruction; and inputting the instruction into a preset simulation environment model for analogue simulation, and calculating and optimizing the time sequence decision model and the strategy generation model based on the composite reward. According to the method, through multi-modal feature fusion and a dynamic attention mechanism, in combination with time sequence decision optimization, the autonomous fire extinguishing capability of the fire-fighting unmanned aerial vehicle in a complex fire scene environment is remarkably improved, and the fire extinguishing efficiency and safety are enhanced.

A full-electronic DFPT simulation optimization acceleration method is used for a full-electronic DFPT simulation calculation task, the simulation calculation task is divided into multiple batches of subtasks, and the method comprises the steps that through a batch processing merging strategy, the multiple independent subtasks are merged into batch tasks enabling GPU thread blocks to be fully loaded, so that the GPU thread utilization rate is increased; for the merged batch tasks, atomic or grid point attributes are uniformly stored in a structured memory allocation mode, and a lock page memory pool is pre-allocated to multiplex a transmission buffer area, so that the memory access efficiency is improved, and memory fragmentization is reduced; and constructing a calculation-transmission assembly line based on a multi-stream assembly line mechanism, and performing overlapped execution of calculation of the merged batch tasks and data transmission of the next batch by using a lock page memory buffer area. According to the method, the thread-level parallel efficiency of the GPU can be improved, the memory layout and multiplexing are optimized, the data transmission waiting time is shortened, and the performance of DFPT simulation under the high-throughput condition is improved.

The invention relates to the cross technical field of industrial intelligent simulation and computational material science, in particular to a phase field simulation method and device based on physical constraint deep learning and electronic equipment, and the method comprises the steps: obtaining phase field parameters of a to-be-predicted material, including an initial phase field value, an initial material mobility tensor and an initial free energy density coefficient, the phase field parameters are input into a pre-trained prediction model, so that a phase field simulation result of the to-be-predicted material is obtained, and the pre-trained prediction model is used for predicting the phase field of the to-be-predicted material based on the improved LSTM network, a preset hybrid forward propagation strategy and a preset parameter coupling mechanism. And training a preset neural network by using a mixed loss function and a sub-path gradient back propagation strategy. Therefore, the problems of low phase field simulation calculation efficiency, physics non-conservation of a data driving model and the like in the related technology are solved, physical equation constraints are embedded into the neural network through a hybrid model architecture, and efficient and accurate prediction of material microstructure evolution is realized.