497 results about "Analogue computation" patented technology

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 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 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 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 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 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.

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.

The invention relates to the technical field of water monitoring and detection, in particular to a simulation system and an early warning method for multi-point dam break flood. The simulation system comprises a data collection module, a data processing module, a model construction module and a simulation calculation module, and the simulation calculation module is used for completing calculation of multi-point dam break flood in the flood dynamic coupling model and outputting a simulation result. Different reference data are unified to the same projection coordinate system through a coordinate conversion algorithm, and terrain splicing dislocation is avoided; the model construction module firstly generates an unstructured grid based on accurate topographic data, and grid nodes are overlapped with actual topographic feature points through a grid boundary optimization algorithm; extracting a grid where a potential overflow point is located, and writing the grid into a parameter file as a dynamic boundary condition; finally integrating geometry, boundaries, parameters and source files to construct a coupling model; the problem that an existing simulation system cannot truly reflect nonlinear phenomena such as turbulence and vortex in the flood routing process is solved.

The invention relates to an icing power transmission line galloping simulation method and simulation system based on a composite time integral. The method comprises the following steps: S1, establishing a nonlinear finite element model of an icing power transmission line; s2, carrying out time domain solution by adopting a composite time integration algorithm; calculating the solution of the moment in two sub-steps according to the solution of the moment, namely from the first sub-step to the second sub-step; the first sub-step adopts a trapezoidal rule for integration, and the second sub-step adopts a three-point backward difference format based on information of three moments; s3, coupling and solving a nonlinear aerodynamic load related to the speed; s4, obtaining the galloping response of the power transmission line; the problems that in a transmission line galloping simulation technology based on a traditional Newmark method, contradiction between calculation efficiency and numerical precision exists in the strong nonlinear coupling problem, accurate tracking of low-frequency galloping and efficient suppression of high-frequency noise are difficult to achieve at the same time under the large time step length, simulation calculation efficiency is low, and convergence is poor are solved.

The invention discloses a photovoltaic power station minute-level power prediction method and system fused with a satellite cloud picture, and belongs to the technical field of data processing and prediction, and the method comprises the steps: obtaining a continuous satellite cloud picture sequence of an area where a target photovoltaic power station is located; obtaining cloud cluster individuals and time-space trajectory data, and extracting time-varying multi-dimensional vital sign parameters of the cloud cluster individuals; inputting the multi-dimensional vital sign parameters into a cloud cluster state evolution prediction model to obtain a cloud cluster state evolution trajectory; obtaining a three-dimensional digital model of the target photovoltaic power station, and generating a super-local irradiance distribution diagram sequence of the target photovoltaic power station based on the state evolution trajectory; and power station electrical characteristic parameters of the target photovoltaic power station are acquired, and a power prediction sequence of the target photovoltaic power station is calculated according to the super-local irradiance distribution diagram sequence. By adopting a means of tracking and predicting multi-dimensional vital sign evolution of cloud cluster individuals and combining a power station three-dimensional model to perform light and shadow simulation calculation, the accuracy of power prediction can be improved.

The invention discloses a simulation method and system for comprehensive utilization of tail water in a rice and shrimp planting and breeding area based on digital twinning, and particularly relates to the technical field of digital twinning and computer simulation. The method is used for solving the problem that an existing digital twinborn model depends on mathematical speculation to cause systematic cognitive deviation of a simulation result during simulation in a large number of areas without monitoring data in a rice and shrimp planting and breeding area. By acquiring monitoring data and a physical topological structure, a simulation area is divided, and a key data blank sub-area is identified; calculating the potential contribution degree of the state change of each key area to downstream monitoring data and the monitoring data change difference caused by the state change of each key area under different hydraulic paths; the simulation uncertainty level of each key area is evaluated according to the analysis result; and finally, adjusting a water quality simulation calculation strategy of a corresponding region in the digital twin model based on the grade, and operating the model to output a tail water comprehensive utilization simulation result. And the reliability of the whole simulation result and the scientificity of decision support are improved.

The invention belongs to the technical field of oil and gas field development engineering and multiphase flow numerical simulation, and relates to a rapid prediction method for gas-liquid two-phase flow key flow parameters of a horizontal gas well. Constructing the mixed momentum equation, the mass conservation equation and the drift velocity constitutive relation into a partial differential equation residual group; secondly, constructing a physical information Fourier operator network architecture, and learning integral operator mapping from working condition parameters to flow field distribution in a frequency domain by using a lifting layer, a Fourier layer and a projection layer; and finally, calculating a partial derivative of an output variable relative to a space-time coordinate by utilizing an automatic differential technology, and constructing a loss function fusing a data error and a physical PDE residual error to carry out model training. According to the method, millisecond-level accurate prediction of the pressure, the liquid holdup and the flow velocity field of the whole wellbore is achieved, and the problems that traditional numerical simulation calculation is long in time consumption, the real-time monitoring requirement is difficult to meet, and a pure data driving model lacks physical consistency are solved.

The invention discloses an ocean internal wave prediction method based on satellite remote sensing data, and the method comprises the steps: firstly obtaining a multi-source satellite remote sensing image of a target sea area, and building a multi-source satellite remote sensing internal wave crest line data set; and then constructing an internal wave numerical model, performing internal wave numerical simulation calculation by adopting the internal wave numerical model in a computational domain range of each two-dimensional section, further obtaining an internal wave generation statistical data set, finally constructing an internal wave generation statistical model, and obtaining an initial internal wave crest line. On the basis, matching an internal wave crest line of the remote sensing image with an initial internal wave crest line to obtain corresponding initial crest line information, an initial arrival time field and generation moments of N two-dimensional sections at a generation source of the Lusong strait, and finally constructing an internal wave initial arrival time error statistical model. And correcting the initial arrival time field to obtain a corrected arrival time field, thereby constructing an internal wave propagation model and realizing prediction of an internal wave crest line.

The invention discloses a sedimentary facies constraint simulation method and device based on three-dimensional dynamic data, electronic equipment and a medium. The method comprises the steps of determining a three-dimensional grid space range, and performing data preprocessing through phase data; determining a calculation diameter and a phase control value range of the three-dimensional spherical model through a variation function; carrying out normal transformation on the phase-by-phase logging data and the seismic data, and then carrying out Bayesian-sequential Gaussian simulation; and performing normal inverse transformation on a simulation result in the grid to finish simulation calculation. Spatial sample points are expanded to the spatial three-dimensional sphere model, so that the value range of to-be-simulated points can dynamically change, and the correlation of sample point values is improved; on the basis of a variation function, under the condition of considering the source direction, data in the three-dimensional sphere model is screened, sedimentary facies are introduced into an algorithm for constraint, the accuracy of sampling point value taking can be guaranteed, geological cognition and sedimentary modes can be followed, and more accurate input data are provided for simulation prediction.

The invention discloses a hydraulic fracturing crack propagation simulation acceleration method and system based on finite element and deep learning coupling. In order to solve the problems of large calculation amount and long time consumption of fracture propagation numerical simulation in the hydraulic fracturing process, the efficient fracture propagation prediction method is developed by combining deep learning sequence modeling and finite element software accurate solution. According to the method, the results of a plurality of first time steps of hydraulic fracturing crack expansion are firstly calculated and obtained through finite element software, corresponding physical field data are exported, deep learning models such as a long-short-term memory network (LSTM) are trained, and rapid prediction of the crack expansion form of the subsequent time steps is achieved; and a prediction result is transmitted back to the finite element software as a new initial condition, and cyclic coupling calculation is carried out. According to the method, computing resources and time required by hydraulic fracturing crack propagation simulation are greatly reduced, and the efficiency and engineering practicability of crack propagation analysis are improved.

The invention relates to the technical field of supply chain monitoring, and discloses an intelligent monitoring and early warning method, device and equipment for a supply chain and a storage medium, and the method comprises the steps: deploying a plurality of types of Internet of Things sensors at each target node in the supply chain, collecting the state data of the supply chain, and executing time sequence analysis and anomaly detection in an edge server, obtaining local early warning information; performing graph neural network analysis based on the local early warning information and a preset supply chain network structure to obtain a node risk representation vector and a risk propagation probability matrix; according to the node risk representation vector and the risk propagation probability matrix, early warning strategy calculation is carried out through a grouped multi-agent deep reinforcement learning algorithm, and an early warning strategy set is obtained; and performing risk propagation simulation calculation on the early warning strategy set and the supply chain network structure to obtain comprehensive early warning decision information. According to the invention, the risk propagation path and rate can be accurately identified, and the accuracy of supply chain risk propagation prediction is improved.

The invention discloses a three-dimensional geological modeling method based on sequential Gaussian simulation, and particularly relates to the technical field of geological modeling, in particular to a three-dimensional geological modeling method based on sequential Gaussian simulation. The method comprises the steps of obtaining and preprocessing geological data, constructing a three-dimensional grid model, analyzing a space structure by adopting an improved variation function integrated with anisotropic parameters, carrying out random simulation by utilizing a sequential Gaussian simulation algorithm and introducing a dynamic conditional mechanism, and meanwhile, integrating multi-source soft data as collaborative variables to apply geological constraints. And finally, post-processing and uncertainty quantification are carried out on a simulation result. According to the method, the description precision of the model on a complex geological structure, the efficiency of simulation calculation and the reliability of multi-source data collaborative application are effectively improved.

The invention provides an enterprise manufacturing resource configuration optimization method based on intelligent interaction and template mapping, and belongs to the technical field of intelligent manufacturing. Comprising the following steps: receiving a resource configuration request input by a user, and performing natural language analysis on the resource configuration request to generate structured resource configuration demand information; according to the structured resource configuration demand information, matching and calling a preset resource configuration template; based on the called resource configuration template, in combination with real-time resource state data in the ERP system, performing analog computation of resource configuration, and generating at least one resource configuration scheme; and feeding back the at least one resource configuration scheme to the user, and receiving feedback information of the user on the resource configuration scheme. User requirements are analyzed through intelligent interaction, an optimization strategy is matched through template mapping, real-time data simulation calculation is combined, feedback is received, and intelligentization, high efficiency and continuous optimization of enterprise manufacturing resource configuration are achieved.

The invention provides a process factory digital twinning implementation method and system based on dynamic number quality analysis, and relates to the technical field of process factory digital management.The process factory digital twinning implementation method comprises the steps that firstly, process link characteristic data and initial material input data of a process factory are obtained, and a process flow model reflecting the relation between process link connection and material transfer is constructed; performing analog calculation based on the technological process model and the initial data to obtain theoretical number quality data, and introducing a material attribute transmission attenuation coefficient; acquiring real-time number quality data of key process links, performing multi-device data fusion processing, and performing comparative analysis on the data and theoretical data to obtain a number quality deviation analysis result containing deviation propagation path information; and a process parameter adjustment instruction containing adjustment effect prediction information is generated according to the number quality deviation analysis result and sent to the control terminal, and dynamic monitoring, accurate analysis and intelligent regulation and control of the process factory are achieved.

The invention relates to the technical field of power system regulation and simulation test, in particular to a digital twinborn-based user load regulation control test system, which comprises a data interaction and instruction receiving module, a digital twinborn simulation module, a strategy effect simulation calculation module and a test report generation module, wherein the data interaction and instruction receiving module is used for receiving a user load adjusting instruction; the digital twinborn simulation module is used for driving a preset user load digital twinborn model to carry out dynamic simulation and outputting real-time response data of the virtual load cluster; the strategy effect simulation calculation module is used for calculating an execution effect index of the user load regulation instruction; and the test report generation module is used for generating a structured test report. According to the invention, by constructing a load adjustment test system based on digital twinning, dynamic simulation, index quantification and conclusion standardization output of the adjustment process are realized, and the efficiency of adjustment strategy verification is improved.

The invention relates to a TA15 titanium alloy single-phase region forging method based on finite element analog simulation. The TA15 titanium alloy single-phase region forging method comprises the following steps that S1, a TA15 titanium alloy cast ingot is smelted; s2, cogging and heating the cast ingot; s3, cogging and forging; s4, collecting and calculating a heat exchange coefficient; s5, heating schedule simulation calculation; s6, heating in a single-phase region; s7, forging process simulation calculation; s8, single-phase region forging; and S9, tissue detection. According to the method, accurate calculation is conducted through the core temperature rise effect generated in the thermal deformation process, the heating system is optimized, the problems of surface oxidation, beta embrittlement and the like of the blank due to too long heating time are avoided, meanwhile, the heating energy consumption cost is remarkably reduced, and the production efficiency is improved.

The invention relates to a reconfigurable metasurface analog calculator based on a lithium niobate film. The reconfigurable metasurface analog calculator comprises a substrate and a lithium niobate unit, the plurality of lithium niobate units are periodically arranged on the substrate to form a metasurface structure, and the cross section of each lithium niobate unit is an isosceles trapezoid; incident light vertically enters the metasurface structure, and the transmission coefficient and phase distribution of the metasurface structure are changed by changing external bias voltage applied to the lithium niobate units, so that conversion and reconstruction of a mathematical operation function are realized; the mathematical operation comprises first-order differential operation, second-order differential operation and integral operation. According to the invention, different external bias voltages are applied to the periodically arranged lithium niobate units, so that the transmission coefficient and phase distribution of incident light by the metasurface structure can be accurately regulated and controlled, and the same metasurface device can be flexibly switched among different mathematical operation functions such as first-order differential, second-order differential and integral. And various calculation functions can be realized without changing a physical structure.

The invention relates to the technical field of continuous steel casting of ferrous metallurgy, in particular to a crystallizer flow field control method based on F value optimization, which comprises the following steps of: calculating the distribution of a molten steel flow field in a crystallizer through numerical simulation based on a target plate blank section size and a preset pulling speed to obtain flow field characteristic parameters, and calculating an F value of a current working condition, whether the F value is in a preset optimization interval or not is judged, if not, the submersed nozzle insertion depth is adjusted till the F value falls into the optimization interval, the optimal technological parameters are determined, judgment can be assisted by combining the maximum speed of the free liquid level, and finally a recommended nozzle insertion depth query table corresponding to different section width and pulling speed combinations is established. According to the method, the technical effects of optimizing the flow field of the crystallizer, accurately determining the optimal insertion depth of the submersed nozzle and providing proper process parameters for different working conditions are achieved.