1969results about How to "Improve calculation accuracy" patented technology

The invention relates to an electric power working environment intelligent positioning and risk management and control method based on a three-dimensional model, and the method comprises the following steps: S1, constructing a three-dimensional model of an electric power working environment, and carrying out the spatial layering and equipment marking of the three-dimensional model, and forming a three-dimensional panoramic working image with attributes; s2, marking positions of operating personnel and equipment in real time, and generating dynamic positioning in a power operation scene; s3, recording and predicting the track of the operator, and pre-warning whether the operator enters a safe area or a dangerous area; a camera is used for collecting images of the operating personnel, non-standard behaviors of the operating personnel are recognized in real time through an AI algorithm, and behavior risk indexes of the operating personnel are generated based on detection results; s4, training a risk prediction model based on sensor data, detecting possible risk events, and obtaining an operation risk index of the equipment; and S5, calculating a comprehensive risk index in real time. According to the invention, the comprehensive safety of personnel, equipment and environment is guaranteed while the safety management efficiency is effectively improved.

The invention relates to the field of shield tunneling, and discloses a shield tunneling control method and system based on a rock-soil particle motion trail, and the method comprises the steps: fusion deployment of various types of high-precision sensors, intelligent anti-noise data collection and preprocessing, precise physical model construction, efficient numerical simulation optimization, and multi-parameter comprehensive detection and correlation analysis. And real-time data-driven adaptive control model construction, control instruction rapid generation and transmission, closed-loop feedback and continuous optimization are realized. According to the method, multiple links including fusion deployment of multiple types of high-precision sensors, intelligent anti-noise processing, physical model construction and the like are covered, all the links have a synergistic effect, data can be accurately obtained, data quality can be guaranteed, a simulation foundation can be provided, comprehensive evaluation, intelligent control and rapid instruction transmission can be achieved, continuous optimization is achieved through closed-loop feedback, and the method is suitable for large-scale popularization and application. And finally, the construction level in all aspects is improved, and complex engineering requirements are met.

The invention provides a central air-conditioning system real-time fault prediction method and system based on digital twinning, and the method comprises the steps: deploying various types of sensors for multi-dimensional detection, and collecting the data of the sensors; edge computing nodes are deployed around the central air conditioning unit; a digital twinborn model based on the central air conditioning unit is constructed according to the preprocessed data, a dynamic view of key performance indexes is provided, and the state of the central air conditioning unit is mapped and simulated in real time; designing a fault prediction model, and predicting and identifying faults; deploying the fault prediction model at an edge computing node and operating the fault prediction model, and performing real-time fault monitoring and diagnosis; when a fault is found, the AI-drive maintains decision support, and automatically adjusts a fault prediction model parameter or arranges a maintenance strategy; and constructing a learning loop based on performance feedback, and optimizing a fault prediction model and a maintenance strategy. Quantum calculation, the digital twinning technology and edge calculation are fused, and the operation efficiency and reliability of the central air conditioning system are remarkably improved.

The invention relates to the technical field of mineral resource reserve simulation, in particular to a geological mineral resource reserve simulation method. The method comprises the following steps: carrying out multi-source heterogeneous data acquisition on a target geological area, and carrying out comprehensive feature extraction to obtain a comprehensive data set; constructing an initial static geologic model according to the comprehensive data set to obtain the initial static geologic model; building a multi-physics field coupling model according to the initial static geologic model to obtain a multi-physics field coupling model grid; performing metallogenic fluid migration and enrichment simulation according to the multi-physics field coupling model grid to obtain a metallogenic fluid migration simulation result; carrying out geologic body geometrical morphology updating on the initial static geologic model according to a metallogenic fluid migration simulation result to obtain a dynamic geologic model; and performing reserve prediction model training according to the dynamic geologic model to obtain a reserve prediction model. According to the method, the mineral resource reserves are evaluated more accurately and dynamically through a mineral resource reserve simulation technology.

The invention relates to a high-precision multi-channel phased array radio measurement calibration method and a high-precision multi-channel phased array radio measurement calibration system. The method comprises the following steps: carrying out electromagnetic characteristic modeling on N array element channels and an antenna housing of the multi-channel phased-array antenna to obtain a channel measurement model; carrying out electromagnetic field numerical calculation on the multi-channel phased-array antenna to obtain an initial error data matrix; mapping the initial error data matrix to a two-dimensional weight matrix, and performing optimal matching solution on the two-dimensional weight matrix to obtain a theoretical calibration coefficient matrix; connecting the standard gain antenna and the multi-channel phased-array antenna by adopting a vector network analyzer, and performing far-field scanning measurement on the multi-channel phased-array antenna to obtain actually measured amplitude-phase characteristic data; and performing weighted least square iterative calculation on the actually measured amplitude-phase characteristic data and the theoretical calibration coefficient matrix to obtain a target calibration compensation coefficient. According to the invention, accurate measurement of the amplitude-phase characteristics of the multi-channel phased-array antenna is realized, and the stability of the calibration process is improved.

The invention discloses a complex terrain wind resource assessment method based on measured data correction of an anemometer tower, and the method comprises the steps: simulating a complex terrain wind field based on a mesoscale WRF mode, and obtaining the mesoscale data of an innermost layer nested domain containing a wind power plant; according to the mesoscale data obtained in the step S1, microscale computational domain boundary inflow information is extracted, and initial prediction field information of the complex terrain wind field is obtained based on a mesoscale-microscale coupling mode; according to the anemometer tower position and the flow field characteristics, determining an anemometer tower reference height used for data correction and a corresponding observation wind speed and turbulence intensity; and S3, correcting the WRF-CFD numerical simulation result obtained in the step S2 by adopting the related data of the anemometer tower determined in the step S3 to obtain corrected wind speed and turbulence information of the wind field of the complex terrain. According to the method, the medium-micro scale coupling numerical simulation result can be improved, and the accuracy of wind resource evaluation is greatly improved.

The invention is suitable for the field of logistics transportation, and provides a hazardous chemical substance transportation path planning method and system, and the method comprises the steps: collecting road network information, road condition real-time data, weather data, population density distribution data, environment sensitive point distribution data, and hazardous chemical substance characteristic data; based on the collected data, establishing a risk assessment model, and performing risk assessment on each preset transportation path; constructing a multi-objective function, and distributing a corresponding weight for each objective; and searching and generating an optimal or suboptimal transportation path in the road network based on the multi-objective function, and when the change of the road condition data or the weather data exceeds a preset threshold value, performing risk assessment and path planning again, and regenerating the optimal or suboptimal transportation path. And the transportation data security is ensured. A path can be adjusted in real time to cope with road conditions and weather changes, the transportation risk is reduced, and the transportation cost, time and risk are balanced.

The invention discloses a high-precision granary volume calculation method and system, and relates to the technical field of laser radar technology and point cloud processing, and the method comprises the following steps: data collection; laser radar equipment is used for carrying out omnibearing scanning on a target object, and a laser point cloud data set is obtained; preprocessing the data; performing point cloud segmentation; object recognition; point cloud precision compensation: performing precision compensation on the point cloud data, the precision compensation including error compensation of laser radar precision along with distance and temperature changes; volume calculation: performing volume calculation on the recognized regular object by adopting a volume calculation formula; for the recognized irregular object, a three-dimensional grid model of the object is created through a three-dimensional model reconstruction algorithm, the grid model is adjusted, and then the volume of the grid model is calculated; according to the method, optimization is carried out from the two aspects of radar distance and temperature compensation, an error model is established, in the model, primary functions are designed from the two aspects of temperature and distance, and the precision is improved from the two aspects at the same time.

The invention relates to a river water ecological space global monitoring method and system, and the method comprises the steps: recognizing and segmenting a water body image from an RGB image photographed by an unmanned plane based on a U-Net deep learning model, and forming a water body recognition image; converting and reconstructing the RGB image aerially photographed by the unmanned aerial vehicle into a water body hyperspectral image based on a hyperspectral information continuation reconstruction model; the U-Net deep learning model extracts a multispectral segment sequence of pixel points of a water body in the water body hyperspectral image and sends the multispectral segment sequence to the classification regression model; and calculating the multispectral segment sequence based on a classification regression model so as to label a concentration classification result of chlorophyll a in a water body hyperspectral image, thereby realizing visualization of chlorophyll concentration. The method gets rid of limitation of technical means and time acquisition of hyperspectral data acquisition, and realizes the effect of acquiring river global images at any time and at any moment.

The invention relates to the technical field of data processing, and discloses a wind wave flow prediction method and system based on meteorological simulation. The method comprises the following steps: performing atmospheric mode simulation on initial condition data and boundary condition data to obtain sea surface wind field data and atmospheric parameter data; performing post-processing and interpolation on the sea surface wind field data and the atmospheric parameter data to obtain wind wave model driving force field data; carrying out wind wave model coupling and simulation on the wind wave model driving force field data to obtain wind wave prediction result data; performing ocean current model coupling and simulation on the wind wave prediction result data and the wind wave model driving force field data to obtain ocean environment prediction result data; and performing data fusion and data assimilation processing on the marine environment prediction result data, the storm prediction result data and the storm model driving force field data to obtain storm flow prediction data, thereby improving timeliness and accuracy of storm flow prediction based on meteorological simulation.

The invention discloses a regional risk prediction method and system based on toxic gas diffusion analysis, and provides a regional risk prediction method based on toxic gas diffusion analysis on the basis of source item traceability prediction of a leakage source. The content of the invention mainly relates to development and improvement of a gas diffusion model, data acquisition and processing, construction of a risk prediction model, uncertainty analysis, research of an emergency response strategy, case analysis and verification and system integration and demonstration application, and through comprehensive consideration of factors such as gas properties, meteorological conditions and topographic features, prediction accuracy is improved, and the risk of the gas diffusion model is predicted. Scientific basis is provided for risk management, and practicability and popularization of the prediction system are finally realized.

The invention discloses an instruction execution acceleration method based on a GPGPU and a GPGPU architecture, and relates to the field of high-performance computation.The method comprises the steps that a thread execution task output by a host compiler is received, and the thread execution task is scheduled according to the current idle state of a computing unit; capturing instruction data output by the host compiler, and executing concurrent instruction decoding according to the corresponding instruction type to obtain a machine code corresponding to the instruction data; obtaining an operand corresponding to the instruction data, and generating a to-be-transmitted instruction based on the machine code corresponding to the instruction data and the operand after secondary verification; according to the calculation complexity, determining an optimal calculation unit, and sending the to-be-launched instruction to the optimal calculation unit; and receiving an execution result of the to-be-transmitted instruction, and returning the execution result to the terminal. The thread execution task is dynamically scheduled according to the idle state of the computing unit, so that computing resources can be fully utilized, and the operation efficiency is improved.

The invention discloses a cable fault positioning method and system based on reflected wave decomposition and kurtosis sorting, and aims to solve the problem that a cable fault is difficult to detect under a low impedance change rate. The method comprises the steps of transmitting an incident excitation signal to a cable to obtain a first reflection signal in the cable; an improved VMD algorithm is adopted for decomposition to obtain K IMF components, and the IMF with the maximum kurtosis is selected as a second reflection signal; acquiring a time-frequency distribution curve of the second reflected signal by using short-time Fourier transform and frequency spectrum redistribution technologies; and constructing a two-dimensional energy-time curve by extracting an energy value and time under the frequency of the maximum energy amplitude point, and determining a fault position. The system is composed of an incident excitation module, a decomposition module, an STFT-RSP module and a fault positioning output module, and the positioning method is achieved. According to the method, the limitation of a traditional spread spectrum time domain reflection positioning method in cable fault detection is effectively overcome, and an efficient and accurate instructive technology is provided for cable fault positioning and detection.

The invention discloses a multi-light-spot centroid extraction method and device based on an FPGA, a medium and a product, and relates to the field of digital image processing, and the method comprises the steps: firstly, caching image line data through a first-stage FIFO and a second-stage FIFO, forming three continuous groups of image line data streams, effectively solving the problems of data synchronization and processing rate matching, and improving the data processing efficiency; then, effective pixels of the light spots are accurately determined by means of a pixel threshold value, and different light spot areas are accurately divided by means of line continuity judgment and boundary marking, so that repeated recognition and error recognition of the light spots are avoided, and the accuracy of light spot recognition is greatly improved. And finally, according to the weighted average positions of the gray values of the effective pixels of all the light spots, the target centroid coordinates of the target light spots are calculated, the internal gray difference of the light spots is fully considered, and compared with a conventional method, the light spot energy distribution center is more accurately reflected, so that the calculation precision of the light spot centroid is remarkably improved.

The invention provides a zipper simulation method and related equipment, a zipper comprises a tooth chain and a puller, the method comprises the following steps: obtaining dynamic performance parameters of the puller pulling the tooth chain, and generating a simulation pulling process corresponding to the pulling process according to the dynamic performance parameters; obtaining simulation penetration depths and initial penalty coefficients between simulation structure contact surfaces of the simulation zipper corresponding to the simulation pulling process and the pulling process; updating the initial penalty coefficient according to the depth change of the simulation penetration depth; according to the simulation penetration depth and the updated initial penalty coefficient, obtaining the normal force of the contact surface of each simulated structure; according to the friction coefficient and the relative sliding speed, obtaining a dynamic friction coefficient between the contact surfaces of the simulated structures; according to the normal force and the dynamic friction coefficient, the zipper light sliding force in the simulation pulling process is obtained, and the detection accuracy and calculation efficiency of the normal force (the component of the contact force) of the microscopic contact behavior between the puller and the toothed chain and the zipper light sliding force can be improved.

The invention discloses a foot drop assisting method based on a flexible ankle joint exoskeleton, and belongs to the field of rehabilitation medical assistance. According to the method, an unhealthy side ankle joint angle is input into a self-adaptive oscillator to learn related parameters in the self-adaptive oscillator, and an affected side ankle joint angle is predicted; meanwhile, a CNP model is adopted to predict the ankle joint angle of the shank on the affected side according to the gait phase estimated by the self-adaptive oscillator, two prediction results of the ankle joint angle of the shank on the affected side are subjected to weighted fusion to finally obtain the expected angle of the ankle joint on the affected side, and the fused ankle joint angle prediction output is more stable and continuous. And a weighted fusion result is smoothed. According to the method, the normal walking mode based on the unhealthy side ankle joint angle of the patient can be dynamically learned online in real time, and personalized affected side ankle joint angle reference tracks at each moment are generated for different patients, so that the abnormal motion mode of the patient is inhibited, and the patient is guided to reach the normal gait in the rehabilitation training process.

The invention provides a high-resolution positioning method for noise source imaging. The high-resolution positioning method comprises the following steps: designing a sensor array layout and selecting sensors; a multi-channel acquisition synchronous acquisition sensor and records signals to obtain acquisition data; performing signal processing on the acquired data by using adaptive filtering, machine learning calibration, time-frequency analysis and intelligent beam forming technologies to obtain standard data; based on the standard data, performing high-resolution direction estimation by using an optimized multi-signal classification algorithm, and removing a sidelobe effect by using an optimized CLEAN algorithm to complete processing of the standard data; and based on the processed standard data, reconstructing a spatial distribution image of the noise source by using an inversion technology, and dynamically adjusting the resolution of the reconstructed spatial distribution image by using an adaptive algorithm to realize high-resolution positioning of noise source imaging. According to the invention, high-resolution positioning can be realized, the reliability and precision of noise detection are improved, and noise control and management are more effective.

The invention discloses a method and a system for predicting a temperature field of a high-capacity energy storage lithium ion battery. The method comprises the following steps: acquiring temperature values of a few temperature measuring points of the lithium ion battery to be predicted; inputting the obtained temperature value into a preset physical information-neural network model for prediction to obtain a temperature field of the lithium ion battery cell to be predicted; wherein the preset physical information-neural network model comprises a basic neural network part and a physical information fusion part, and the physical information fusion part adds a battery heat transfer differential equation and a battery boundary condition as physical information constraints in a neural network training process; the loss function in the neural network comprises boundary loss, partial differential equation loss and observation data set loss. The system comprises a data acquisition module and a data processing module. According to the method, the neural network algorithm is combined with the lithium battery heat transfer model, and temperature field inversion of the high-capacity battery is realized.

The invention discloses a three-dimensional finite element simulation method for a complex slope excavation unloading process, which comprises the following steps of: 1) constructing a three-dimensional model of a complex slope, and establishing a finite element grid; 2) inputting boundary conditions of the model and material parameter information of each stratum; 3) calculating and simulating an initial stress field of the slope before excavation; (4) according to the initial stress field, the unloading load of the slope excavation part is calculated; (5) finite element calculation is conducted on stress deformation of the excavated slope; according to the method, the high-precision three-dimensional finite element model is established, various material parameters and actual boundary conditions are combined, the stress change and the unloading effect of the slope in the excavation process are simulated, the stress redistribution in the unloading process is accurately reproduced, the prediction precision of local instability and stress concentration areas is improved, and the prediction accuracy of the slope is improved. Therefore, a scientific basis is provided for safety assessment of slope excavation.

The invention belongs to the technical field of aircraft design, and discloses an airfoil structure virtual test loading analysis method based on digital simulation, which comprises the following steps of: firstly, carrying out detail finite element modeling according to an airfoil structure digital model, the attribute of the connecting piece is calculated and assigned according to the simplification principle of a bush unit, and the center coordinate of a loading point is determined according to the position of an adhesive tape or a pull-press block in the test partial load scheme; simulation analysis is carried out after loading is completed, loading is carried out according to the initial load direction, the load is discretized to a single loading point, then the single loading point is recorded, coordinates of a center node of the loading point and coordinates of an actuator connecting point after deformation are obtained, then the direction of a loading vector is obtained, all loading points of the simulation model are loaded, and a simulation result is obtained. After analysis is carried out, linear / nonlinear static force, buckling and post-buckling calculation is carried out; analyzing a stress-strain identification test risk position according to a calculation result, and pasting a strain gauge. The method can guide the design of a loading scheme in an airfoil structure test, improves the calculation precision, recognizes the test risk, avoids the loss of a test piece, and reduces the test cost.

The invention discloses a multi-AGV task scheduling method and device based on a multi-strategy whale optimization algorithm combined with reinforcement learning. The method comprises the following steps: S1, establishing a mathematical model for multi-AGV task scheduling; s2, taking the balance between the path cost and the AGV vacancy rate as an optimization index, and constructing an AGV scheduling optimization model; s3, combining a multi-strategy whale optimization algorithm with a reinforcement learning method framework, and determining a Markov decision process including state, action, reward and value definition; and S4, solving the AGV scheduling optimization model by adopting a multi-strategy whale optimization algorithm combined with reinforcement learning. According to the method, the multi-strategy improved WOA is realized by initializing the population through chaotic mapping, adjusting the non-convergence factor and introducing reinforcement learning to update the whale individual position updating strategy, the calculation precision of the WOA is improved, the algorithm convergence is accelerated, the capacity of the WOA for jumping out of local optimum and searching for global optimum is enhanced, a feasible scheme is provided for multi-AGV task scheduling, and the method is suitable for large-scale popularization and application. And the method has certain advancement and feasibility.

The invention discloses a method for predicting the service life of a forklift frame structure, a frame serves as a forklift main stress component, other systems are all installed on the frame, the method obtains a stress-time history curve of the position, most prone to fatigue failure, of the forklift frame under one operation cycle through stress testing of the forklift frame under the specific operation working condition, and the service life of the forklift frame structure is predicted according to the stress-time history curve of the position, most prone to fatigue failure, of the forklift frame. Through data processing and rain flow counting, stress-time history data of a position most prone to fatigue failure are counted as stress amplitude, average stress and corresponding cycle times in a time period, and accumulated damage in one operation cycle is calculated through an S-N curve of a material, an average stress correction formula and a two-dimensional stress spectrum. Therefore, the fatigue life of the forklift frame is obtained, a life threshold value is set, and early warning is carried out on the life state of the forklift frame.

The invention discloses a CFD-based liquefied natural gas after-condenser structure optimization method and device. The method comprises the steps that an initial model is established based on an assembly drawing, and geometric pretreatment is completed; extracting a fluid channel, segmenting and selecting a computational domain, and carrying out local encryption on a key region; generating a polyhedral grid by using a grid division tool, and completing grid independence verification; importing the polyhedral mesh into a solver, and carrying out model setting; boundary and wall surface conditions are set, and solver parameters are configured; setting an initial flow field through a hybrid initialization method, and establishing a flow monitoring window and a temperature monitoring window to adjust simulation until convergence; analyzing the temperature cloud picture and the flow vector diagram, and adjusting the structure of the baffle plate; the tower diameter and the height of the filler layer are adjusted according to a physical quantity field change curve of the filler layer, and the performance of the recondenser is continuously improved. The problem of how to deeply study the flow heat transfer rule in the after-condenser and obtain the flow field distribution characteristics is solved, so that the working performance of the after-condenser is improved.

The invention provides an aeolian vibration analysis method for a power transmission line steel tube tower, and the method comprises the steps: S1, obtaining the operation monitoring data of the power transmission line steel tube tower, and historical wind speed meteorological data; s2, sensitivity analysis is carried out on a plurality of different parameters possibly influencing the amplitude of the tower, and main parameters causing vortex-induced vibration response of the steel tube tower component are determined; s3, simulating a typical steel tube power transmission tower component model by using the refined three-dimensional finite element model, and analyzing the natural vibration frequency, the vibration mode curve, the critical wind speed and the maximum amplitude of the component; and S4, establishing vortex-induced vibration models of a single steel pipe and adjacent steel pipes, realizing a dynamic grid effect in the aeolian vibration process based on a nested grid technology, developing a fluid-solid coupling calculation UDF program in the vortex-induced vibration process, and embedding the fluid-solid coupling calculation UDF program into FLUENT software to reproduce a vortex-induced vibration response process of a complex working condition. According to the method, theoretical analysis and numerical simulation are combined to effectively solve the problem of aeolian vibration response evaluation of the high-voltage transmission line steel tube tower, and the calculation efficiency and precision are high.

The invention belongs to the field of oil-gas exploration seismic data processing, and relates to a first-motion wave travel time chromatography velocity inversion method, system and device and a medium. According to the method, the first arrival travel time and the grid unit travel time are acquired, and the minimum grid unit travel time is selected; an initial velocity field closer to a real structure is established through micro-logging constraint so as to replace a conventionally used gradient velocity field. The initial velocity field and the first arrival travel time are combined to construct the target function, the slowness information and the regularization information are added in the target function, the multi-information constrained target function is obtained, and the inversion stability and the calculation precision are improved. And adopting a Gaussian-Newton method to minimize the target function of the multi-information constraint, solving the minimized target function of the multi-information constraint, and carrying out iterative updating on a solving result to obtain an inversion velocity field. Explicit calculation and sensitive kernel function storage are not needed, the method is more suitable for large-scale data processing, and the calculation time of speed inversion and the requirement for storage space are reduced.

The invention provides a method and device for determining the collision probability of a low-altitude aircraft to a ground obstacle, and relates to the field of aircraft collision. The method comprises the following steps: determining relative position vectors of a mass center position of a low-altitude aircraft in a three-dimensional Cartesian coordinate system corresponding to a center position of a ground obstacle in a longitude direction, a latitude direction and a gravity reverse direction; determining a relative position error matrix of the low-altitude aircraft and the ground obstacle based on an error value between the nominal flight path data and the actual flight path data of the low-altitude aircraft; processing the relative position vector and the position error matrix corresponding to the reverse direction of the gravity to obtain a first integral parameter and a second integral parameter in the reverse direction of the gravity; and calculating the relative position vectors corresponding to the longitude direction and the latitude direction, the first integral parameter and the second integral parameter to obtain the collision probability of the low-altitude aircraft and the ground obstacle. According to the invention, the complex three-dimensional integral operation is simplified, and the calculation efficiency and precision are improved.

The invention relates to the technical field of territorial space planning, in particular to a territorial space planning index calculation method and system based on a large language model. Deriving and outputting the name of the calculation tool and the actual value of the input parameter of the calculation tool through the large model, and generating a corresponding space calculation instruction; executing the space calculation instruction to obtain a corresponding index calculation result; and inputting the index calculation result and the corresponding first task cue word into the large model, and interpreting through the large model to obtain a corresponding territorial space planning result. By implementing the method, the accuracy, timeliness and reliability of land space planning index calculation can be improved.

The invention discloses a method for determining metal fuel multi-phase structure irradiation microstructure evolution and performance change. Related physical property parameters are calculated through molecular dynamics and a first principle, and a diffusion coefficient is corrected by utilizing an irradiation acceleration theory to obtain a diffusion coefficient under a metal fuel irradiation condition; the method comprises the following steps: constructing a metal fuel polycrystalline multiphase organization structure evolution phase field model, defining phase field variables required in the model, and determining an evolution equation of the phase field variables; calculating to obtain a phase-field variable initial value, then solving the phase-field variable evolution equation, and obtaining a phase-field variable numerical solution; the phase field variable numerical solution is subjected to visualization processing, and a metal fuel polycrystalline multiphase structure distribution diagram is obtained; calculating the size and distribution of an irradiation structure in the metal fuel to obtain an evolution rule of the irradiation structure along with fuel consumption; and calculating the thermal conductivity, swelling and stress in the metal fuel by using the size and distribution of the irradiation structure in the metal fuel, and obtaining the change rule of the performance of the metal fuel along with fuel consumption.

The invention provides a low-voltage transformer area line loss calculation method and device, electronic equipment and a medium, and the method is applied to a three-phase Internet of Things meter of a target low-voltage transformer area, and comprises the steps: reading the electric quantity of the three-phase Internet of Things meter and the electric quantity of each single-phase meter, and obtaining the electrical characteristic quantity of the target low-voltage transformer area; calculating a first line loss result based on the electric quantity of the three-phase Internet of Things meter and the electric quantity of each single-phase meter; inputting the electrical characteristic quantity into a pre-trained line loss calculation model to obtain a second line loss result; wherein the line loss calculation model is obtained by training a random forest model based on an improved bat algorithm; and performing weighted calculation on the first line loss result and the second line loss result to obtain a line loss result of the target low-voltage transformer area. According to the invention, the line loss calculation precision is improved.

The invention relates to a non-signaling test-based frequency offset calibration method, system and device and a medium, and the method comprises the steps: carrying out the parallel non-signaling frequency offset test of a plurality of target chips, collecting the output waveforms of the target chips, and determining the frequency offset values of the target chips according to the output waveforms; performing deviation analysis on the frequency deviation value, and calculating a corresponding frequency deviation calibration value based on the frequency deviation value and a preset frequency calibration coefficient under the condition that the frequency deviation value reaches a preset frequency limit range; writing the frequency offset calibration value into a corresponding target chip, and performing parallel frequency offset calibration; and carrying out calibration degree verification on the target chip after frequency offset calibration so as to confirm whether the frequency offset value of the target chip accords with a preset standard frequency. The method and the device have the effect of improving the frequency offset calibration efficiency of the chip.