45results about How to "The calculation process is simple" patented technology

This invention discloses a method and system for predicting the dissolution time of bubbles in oil-paper insulation, belonging to the field of electrical equipment insulation technology. The method includes: calculating the amount of various gases dissolved per unit volume of oil after the gas dissolution in oil reaches equilibrium; using 47℃ as a reference temperature, extrapolating this data to other temperatures and solubilities using the Arrhenius relation, and calculating the diffusion coefficients of various gases in oil at each temperature; calculating the diffusion coefficients of various gases in paper at each temperature; establishing a prediction model for the dissolution time of bubbles in oil-paper insulation, and calculating the dissolution time. This invention considers three factors—gas solubility in oil, diffusion coefficient in oil, and diffusion coefficient in paper—that vary with temperature and gas type, and calculates the dissolution time of a single stationary bubble in oil-paper insulation. This effectively reduces the risk of bubbles during transformer operation and can be widely applied to oil-paper insulation systems with the same kinetic description.

The invention provides a high-reliability power supply mode selection method for a power distribution network. The method comprises the following steps: establishing a quantitative evaluation index system comprising a grid structure, an equipment level, technical application and management capability dimensions; for each power supply wiring mode, an improved success flow method is adopted to calculate a reliability quantitative index of the power supply wiring mode according to a topological structure, a grid structure and equipment level parameters of the power supply wiring mode; meanwhile, based on the index system, the economic quantitative index of the power supply wiring mode is calculated by adopting a full life cycle cost method; extracting a forward index and a reverse index based on the reliability quantitative index and the economical efficiency quantitative index; constructing an improved CCR model of a data envelope analysis method, and taking the reverse index as input and the forward index as output to obtain a quantitative efficiency index; and calculating a comprehensive recommendation coefficient according to the quantitative efficiency index, and taking the power supply wiring mode with the highest comprehensive recommendation coefficient as an optimal power supply wiring mode. According to the method provided by the invention, the reliability and economy are integrated, and the optimal power supply mode is quantitatively recommended.

This application discloses a method for calculating the vibration isolation of ship vibration isolation devices based on isolator type spectrum data, relating to the field of ship vibration isolation design. The method includes: acquiring the type spectrum data and impedance test data of the target isolator; determining the stiffness and damping parameters of the target isolator based on the type spectrum data and impedance test data; constructing a finite element model of the ship vibration isolation device; inputting the stiffness and damping parameters of the target isolator into the finite element model for calculation, and obtaining the vibration isolation calculation results of the target isolator. This method can efficiently utilize isolator type spectrum data to conduct vibration isolation calculation and evaluation of ship vibration isolation devices during the ship design phase, avoiding resonance between the natural frequency of the vibration isolation device and the excitation frequency of the equipment, and improving the vibration isolation effect.

The application discloses a short video advertisement click rate scoring and sorting method, system, device and medium, and belongs to the technical field of short video scoring and sorting, which comprises the following steps: performing feature extraction on a short video advertisement and performing pretreatment to obtain image features and audio features; processing the image features and the audio features to obtain first aggregation features and second aggregation features and processing the first aggregation features and the second aggregation features to obtain popular features and unpopular features; constructing a difference score according to the popular features and the unpopular features; calculating a matching degree score of the first aggregation features and the second aggregation features, and combining the difference score to construct a predicted click rate score of the short video advertisement; and under the constraint of a loss function, using a trained neural network model to perform click rate offline sorting on the short video advertisement. The application solves the problems that the prior art relies on user evaluation information and the traditional video quality evaluation is not backward in the scoring mode.

The invention provides an electrical line device mechanical calculation tool and a use method thereof. The electrical line device mechanical calculation tool comprises a data input module, a calculation processing module and a result output module. Wherein the data input module is used for acquiring an initial parameter of a to-be-processed electrical line device, and transmitting the initial parameter to the calculation processing module; the calculation processing module is used for calculating mechanical parameters of the to-be-processed electrical line device based on the initial parameters and transmitting the mechanical parameters to the result output module; and the result output module is used for outputting the mechanical parameters. According to the method, the problems of tedious calculation process, low precision and limited applicability of the existing scheme for manually calculating the mechanics of the electrical circuit device can be solved.

The application discloses a dark underwater image processing method based on color space conversion, which comprises the following steps: firstly, pre-acquired underwater images are down-sampled to reduce the image size and copied into two images; secondly, color space conversion is performed on the two parts of underwater images respectively; thirdly, an underwater optical imaging image model is established, and the first converted underwater image is processed by using an image deblurring algorithm to obtain a clear image with high contrast; fourthly, an improved white balance algorithm is used to process the deblurred image; fifthly, a nonlinear channel prior is introduced into a traditional image deblurring model to minimize the item to eliminate the motion blur problem caused by water flow fluctuation; sixthly, the obtained accurate blur kernel and potential clear image are iterated with the initial restored image to obtain a final restored image; and finally, the two images are fused, re-sampled and outputted to obtain a final clear underwater image. The application can better realize image restoration, improve the brightness and contrast of the image and improve the recognition degree.

This application discloses a stent selection device and a computer-readable storage medium. The device performs the following operations: 3D reconstruction of target image data containing cerebral blood vessels to obtain a 3D model of the cerebral blood vessels; based on the 3D model, generating a radius curve before dilation, and based on the radius curve, obtaining the stenosis parameters of the narrowed vessel segment, and determining the original diameter stenosis rate based on the stenosis parameters; based on the original diameter stenosis rate, simulating the balloon expansion process through mathematical interpolation and continuity constraints to obtain a radius curve after dilation, and based on the radius curve after dilation and the radius curve before dilation, obtaining a radius change curve; establishing a multi-layer biomechanical model, and based on the radius change curve, determining the radial support force distribution curve, and selecting stent models matching the support force distribution curve from a stent database. Using the solution of this application, the accuracy and computational efficiency of stent selection can be improved.

This application provides a method, apparatus, equipment, and storage medium for identifying agricultural irrigation districts based on the irrigation-enhanced evaporation stress index (IESI), relating to the fields of agricultural water resources monitoring and management and remote sensing applications. The method includes: constructing a multi-source remote sensing and auxiliary dataset for the target area; calculating potential evapotranspiration and actual evapotranspiration and coupling them with a vegetation growth stability factor to obtain quarterly IESI data for the target area; determining irrigation identification thresholds applicable to plains and mountainous areas respectively; and discriminating the IESI of the target area to generate a binary spatial distribution map of irrigated and non-irrigated areas. This application solves the problems of existing irrigation area identification methods in large-scale applications, such as reliance on statistical data or large training samples, strong empirical thresholds, insufficient adaptability to terrain and regional differences, and complex calculation processes. It achieves irrigation behavior identification centered on evaporation stress response, ensuring computational efficiency while also considering spatial accuracy and regional adaptability.

The application discloses a chloride-based multi-parameter humidity and conductivity comprehensive calculation method, comprising the following steps: S1, collecting basic parameter information of chloride materials, and performing relevant data arrangement, and comparing after entering a system; S2, then setting an environmental humidity change range, and obtaining moisture absorption and surface water layer thickness under different relative humidity conditions; S3, establishing a relevant coupling model, and considering ion mobility, charge transmission channel number and interface polarization effect of the chloride materials under different humidity; S4, introducing a multi-parameter regulation factor, correcting environmental temperature, pressure and material surface state, and obtaining correct data; the method has high application prospect and popularization value.

This invention relates to the field of mathematical modeling technology, and more particularly to a method, apparatus, device, and computer-readable storage medium for predicting the remaining lifespan of lithium-ion batteries. The method includes: establishing a segmented empirical degradation model based on inflection point data of the lithium-ion battery; obtaining an initial prediction result of the remaining lifespan of the lithium-ion battery according to a preset particle filter algorithm and the segmented empirical degradation model, and obtaining an original error sequence based on the initial prediction result; determining a reconstruction error sequence corresponding to the capacity of the lithium-ion battery according to the original error sequence and a preset discrete wavelet transform algorithm; constructing a prediction model using a support vector regression algorithm based on the reconstruction error sequence, and correcting the initial prediction result based on the prediction model to determine the final prediction result of the remaining lifespan. This invention improves the prediction efficiency and accuracy of the remaining lifespan of lithium-ion batteries.

This application relates to a method, apparatus, and computer device for detecting low-lying obstacles. The method includes: installing a single-line lidar directly behind a robot and an ultrasonic radar directly below the single-line lidar; extracting single-line lidar data and ultrasonic radar data respectively to obtain a first obstacle target perceived by the single-line lidar and a second obstacle target perceived by the ultrasonic radar; matching the first and second obstacle targets, identifying obstacles that fail to match as low-lying obstacles, continuously tracking the low-lying obstacles and outputting their positions; and outputting the estimated position of the low-lying obstacle when it reaches the common blind spot of the single-line lidar and ultrasonic radar. This method achieves more accurate and convenient low-lying obstacle detection with fewer sensors.

The invention discloses a data processing method, computer equipment, a readable storage medium and a program product, and relates to the technical field of data processing.The data processing method comprises the steps that initial data is acquired; obtaining a conversion configuration rule; querying a generation logic for generating target data by using the initial data in the conversion configuration rule; and calling a transfer function corresponding to the generation logic from the transfer function set as a target function, and executing the target function on the initial data to obtain target data. The technical problem of data processing mode solidification caused by hard codes is solved, and the technical effect of improving data processing flexibility so as to improve data processing performance is achieved.

The application discloses a low-voltage power distribution network abnormal state identification method and device, a storage medium and equipment. The method comprises the following steps: calculating the current deviation of each monitoring point according to the three-phase current of each monitoring point in the low-voltage power distribution network system; mapping the current deviation into a quantum bit superposition state, and constructing the wave function of the low-voltage power distribution network system through a multi-level quantization direct product operation according to the quantum bit superposition state of each monitoring point; determining the Hamiltonian according to the state variable and the topology configuration parameter of each monitoring point in the low-voltage power distribution network system; calculating the energy expectation value of the low-voltage power distribution network system according to the wave function and the Hamiltonian; comparing the energy expectation value with the preset energy range, and identifying the abnormal state of the low-voltage power distribution network system according to the comparison result. The method of the application realizes accurate and efficient identification and early warning of the system-level abnormal state, and overcomes the defects of high misjudgment rate and difficulty in depicting the correlation relationship of the traditional method.

The application discloses a power electronic distribution network transformer propagation model optimal adaptation method and system, and the method comprises the following steps: calculating transformer winding leakage inductance and winding resistance, and transformer excitation inductance and excitation resistance; estimating the equivalent value of each link distributed capacitance; deducing the propagation coefficient of the high-frequency transformer model and the low-frequency transformer model at any frequency under the excitation source; calculating the relative deviation amount of the corresponding propagation coefficient of the high-frequency transformer model and the low-frequency transformer model at different frequencies in the required frequency band of the simulation scene; obtaining the maximum value of the relative deviation amount of the propagation coefficient under all conditions in the required frequency band of the simulation scene; when all the maximum values of the relative deviation amount of the propagation coefficient are less than the set relative deviation limit value, the low-frequency transformer model is selected for the simulation scene, otherwise the high-frequency transformer model is selected; the application has the advantages that the optimal selection of the transformer model is performed under the consideration of the simulation efficiency and accuracy requirements.

The invention discloses a generalized unit commitment calculation method and system considering low-voltage resource and power grid constraint, and belongs to the field of power systems, and the method comprises the steps: obtaining the net load of each node of a power grid corresponding to each moment; taking the net load of each node of the power grid corresponding to each moment as input data, converting the voltage and current of each moment into a safe distance as a label, and training a pre-established neural network; constructing an objective function and a constraint condition of the unit combination, and replacing the constraint condition with the trained neural network; and solving the target function of the unit commitment by using the neural network for replacing the constraint condition to complete the calculation of the unit commitment. According to the method, the trained neural network is adopted to replace the traditional second-order cone constraint, and the replaced constraint is put into the unit combination containing the low-voltage-level power grid, so that the calculation process is effectively simplified, the solving speed of the unit combination is greatly increased, the calculation efficiency is remarkably improved, and decision support can be provided for the operation of the power grid more timely.

The application discloses a self-adaptive gravity compensation trajectory correction method based on a lead angle and belongs to the technical field of aircraft guidance control. The application constructs a dynamic mapping relation between a trajectory deviation and a gravity compensation amount by using the real-time measured lead angle information of the aircraft, adaptively adjusts a gravity compensation coefficient to correct the trajectory, reduces the compensation amount to inhibit the trajectory lifting when attacking a short-distance target, enhances the compensation to offset the falling effect when attacking a long-distance target, improves the problem that the gravity compensation is fixed and does not affect the guidance effect in the traditional method when attacking targets with different distances, reduces the dependence on the difficult-to-measure parameters such as the remaining time in other methods, and improves the striking precision and the shooting range of the aircraft on different targets.

The invention discloses a method for determining the shale gas horizontal well sand-carrying critical gas volume, and belongs to the field of gas reservoir exploitation. According to the method, the liquid holdup and the pressure profile of a horizontal well along a wellbore are calculated by collecting production data, formation data and wellbore structure information of a target well and combining a wellbore liquid holdup model. And then, on the basis of the data, the local fluid flow velocity required by the sand-carrying critical gas flow velocity is calculated, and the fluid holdup under the sand-carrying critical condition is obtained through the relationship between the apparent fluid flow velocity and the real fluid flow velocity. And finally, calculating the sand-carrying critical gas flow velocity through iterative solution, and calculating the corresponding gas flow, namely the sand-carrying critical gas flow, according to the sand-carrying critical gas flow velocity. The method provided by the invention has the characteristics of clear mechanism, easily available parameters and efficient calculation, and can be effectively applied to accurate prediction of the sand-carrying gas volume of the shale gas well.

The invention discloses a multistage correlation preamble detection method and system based on ZC sequence differential coding, related equipment and a medium, and relates to the technical field of wireless communication. The method comprises the following steps: generating a complex ZC sequence, and obtaining a binary real number lead code sequence through differential coding and symbol judgment; performing down-sampling and filtering on the received oversampling signal, and then calculating baseband signal energy through a sliding window; dividing a local lead code and a receiving signal segment into M sub-segments for correlation operation, and accumulating absolute values to obtain a synthetic correlation value; and combining sliding window energy to normalize a synthetic correlation value, and finally detecting a normalized sequence peak value to determine the initial position of a lead code. According to the invention, the low signal-to-noise ratio adaptability and the anti-frequency offset and anti-multipath capabilities are greatly improved, the calculation complexity is reduced, the resource-limited equipment is adapted, the precise detection of the lead code is realized, and the reliable establishment of a communication link under a complex channel is ensured.

A steel shell-UHPC composite beam flexural capacity determination method considering the role of the partition, relates to the field of building design. The steel shell-UHPC composite beam flexural capacity determination method considering the role of the partition is to construct the steel shell-UHPC composite beam structure with partition after four-point loading bending test, determine the failure mechanism, failure characteristics and crack propagation law of the steel shell-UHPC composite beam structure with partition under the action of bending load, establish the cross-section stress diagram of the steel shell-UHPC composite beam structure with partition and calculate the ultimate bending moment. The steel shell-UHPC composite beam structure with partition is established to analyze its failure mechanism, failure characteristics and crack propagation law under the action of bending load, and based on the cross-section stress diagram of the steel shell-UHPC composite beam structure with partition, the ultimate bending moment calculation formula is established to calculate the ultimate bending moment, which can greatly simplify the calculation process while ensuring the calculation accuracy.

The present application relates to the field of geotechnical engineering foundation treatment, in order to improve the calculation efficiency of pile spacing, provide a kind of gravel pile spacing calculation method, system, device, storage medium and product, by obtaining correction coefficient, pile diameter, soil thickness before processing, soil thickness after processing;Based on not considering vibration subsidence and considering the treatment volume of soil body of enlarged pile diameter, and the treatment volume of soil body when considering vibration subsidence and the pile diameter is unchanged, the two are equivalent, calculate the gravel pile spacing, without obtaining soil porosity ratio through experiment, the soil thickness before and after processing is obtained conveniently, so as to simplify the calculation process;It can quickly calculate the pile spacing before preliminary design, significantly save working time;Improve economic efficiency, reduce the cost of early exploration.

The application relates to the technical field of data processing, and discloses a forestry carbon sink satellite remote sensing data correction method. The method is based on a satellite transit time window to implement multi-source time synchronization collection on satellite-borne, airborne and ground measured data, combines micro-meteorological data, digital elevation model data and crown laser radar data to simulate carbon sinks at different elevations at a single time point, and completes simulation correction through remote sensing radiation transmission coupling and eddy correlation data assimilation; further, according to the deviation of measured values and simulated values at different elevations, elevation-specific correction parameters are calculated, a three-dimensional correction parameter field of space-time-elevation is constructed and is adaptively updated; and finally, the correction parameters are used to uniformly correct, weightedly fuse and optimize residuals of satellite-borne data and airborne data. The application can improve the correction accuracy, spatial adaptability and processing efficiency of multi-source forestry carbon sink detection data under complex terrain conditions.

The application discloses a low-bit matrix calculation device with a floating-point scaling factor and a calculation method thereof. The device comprises a scaling factor processing module, an input module, a matrix calculation module, a floating-point accumulation module and an output module. The method first groups and quantizes weight and eigenvalue matrices to obtain quantization values and floating-point scaling factors; then, the scaling factor product is calculated in advance with a single accumulation length as a grouping unit; then, matrix multiplication and accumulation operation is performed, the result is multiplied by the scaling factor product after being converted into an fp16 or bfp16 format, and then accumulated into a final result; and finally, the result is output after being dequantized. The application simplifies the group-wise calculation process, saves hardware resources, guarantees calculation accuracy, meets different scene requirements and improves calculation efficiency by sharing the scaling factor product and using fp16 or bfp16 as an accumulator.

The invention discloses an intelligent calculation method for road subgrade pavement deflection data, and relates to the technical field of road engineering. Comprising the steps that a multi-model fusion intelligent calculation framework is constructed, reinforcement learning and deep learning algorithms are integrated, a desktop application program capable of being independently executed is packaged, intelligent processing of deflection detection data collection is achieved, and the intelligent processing comprises deflection value temperature correction calculation and deflection representative value calculation. According to the method, efficient calculation is carried out on the actually measured roadbed pavement deflection value, the calculation efficiency of the deflection value is remarkably improved by establishing a calculation model and optimizing a calculation process and an algorithm architecture, and the order of magnitude of the calculation efficiency is improved; according to the method, a traditional calculation mode is innovated, and a rapid and accurate solution is provided for roadbed and pavement detection.

The invention relates to the technical field of rail transit, in particular to a rail alignment adjustment control method based on regression technology, comprising the following steps: S1, acquiring JS0; measuring CS1; s2, applying a second-stage constant load P and a T0-T1 temperature difference effect to obtain JS1; s3, verifying JS1 and JS0, and if JS1 and JS0 do not accord with each other, returning to S2; s4, after the second-stage dead load is laid, CS2 is measured, the T0-T2 temperature difference effect is overlaid on CS2, and JS2 is obtained; s5, verifying the JS2 and the JS0, and if the JS2 and the JS0 do not meet the requirements, returning to S2; s6, measuring DY; s7, the temperature T3 is measured, the T3-T2 temperature difference effect is superposed on the CS2, and JS3 and GS1 are obtained; fitting the GS1 by using a multiple regression technology to obtain GS2; s8, calculating the DH, and if the DH does not meet the requirement, returning to the step S2; and S9, steel rail fine adjustment operation is carried out. According to the method, structure manufacturing errors and environmental interference are effectively eliminated, the multiple regression fitting technology is introduced, the calculation process is simplified, the linear adjustment difficulty is reduced, and the steel rail fine adjustment precision is improved.

The invention relates to the technical field of agricultural big data and intelligent traditional Chinese medicine planting evaluation, and discloses a traditional Chinese medicine planting site selection evaluation method based on big data. According to the scheme, sunshine and soil salt content collection devices are deployed in candidate places, sampling is carried out daily to form two types of sequences, and a mean value is solved; normalizing a global minimum value and a global maximum value into standardized sunlight and low salt indexes, and carrying out arithmetic averaging to obtain baseline suitability; a perturbation step length is set according to the range and the candidate number, positive and negative disturbance and amplitude limiting are carried out on sunlight and salt, and four types of disturbance baselines are reconstructed after normalization; the sunlight sensitivity and the salt-containing sensitivity are added according to the ratio of the actual standardized step length to the suitability change, a penalty term is generated by taking the maximum value of the whole, the robustness suitability is obtained through deduction and amplitude limiting, and then quantile grading is used; and finally summarizing the longitude and latitude to form a structured result table.

The application discloses a steam turbine blade residual wall thickness quantitative evaluation method based on alternating electromagnetic field, a phase characteristic detection method based on alternating electromagnetic field, and constructs a universal non-dimensional equation and a phase relationship function, and combines a known thickness test block calibration environment coefficient to realize accurate quantitative evaluation of the steam turbine blade residual wall thickness. The method effectively overcomes the limitations of the existing amplitude method which is susceptible to the lift-off effect, material characteristic fluctuation and background magnetic field interference, significantly improves the detection precision and stability, simplifies the calculation process, enhances the detection applicability and reliability, and provides strong support for predictive maintenance and life management of the steam turbine blade.

The application provides a distribution difference measurement method and system based on convex peak estimation, and a medium, and belongs to the field of random measurement and statistical analysis. The method constructs a concave-convex self-weight measurement system based on simple four arithmetic operations, calculates the cumulative difference and cumulative similarity of each sample point to the whole sample, obtains a composite measured weight, and relativizes it into a concave self-weight and a convex self-weight, takes a convex self-weighted mean as a convex peak estimation, replaces a traditional arithmetic mean, and realizes unbiased measurement of the centralized position of the distribution. The convex self-weighted mean based on the convex peak and the square of the peak difference is used to construct a standard peak difference, to reconstruct the statistics and the variance analysis statistics, and to eliminate the dependence of these statistics on the homogeneity assumption of the variance by using a correction factor to quantify the difference of the three elements. The method does not require normality test and normalizing conversion, and is suitable for single sample test, two sample test, paired test, completely random design and random block design and other statistical scenes of sampling distribution with centralized position.

The invention discloses a terahertz hyperspectral image recovery method and system based on low-rank joint sparsity, and mainly solves the problems that in the prior art, a terahertz hyperspectral image recovery method adopts a single primary function for representation, detail loss and artifacts are easily caused, and the imaging quality is poor in a low sampling rate or low signal-to-noise ratio environment. According to the implementation scheme, the method comprises the following steps: sparsely acquiring a terahertz hyperspectral image of an image domain to obtain actual observation data; defining a sparse forward transformation operator and a sparse inverse transformation operator by using multiple groups of wavelet bases and Dirac bases; the method comprises the following steps: constructing a terahertz hyperspectral image recovery model based on low-rank joint sparse constraint by using actual observation data and a sparse forward transformation operator, and solving the image recovery model by using a primal-dual splitting method to obtain a recovered terahertz hyperspectral image. The terahertz hyperspectral image recovery method realizes high-resolution terahertz hyperspectral image recovery in an extremely low sampling rate or low signal-to-noise ratio environment, and can be used for medical auxiliary diagnosis, security check screening and drug detection.

This invention provides a method, apparatus, and equipment for testing the combined hybrid power system efficiency. Relating to the field of vehicle testing technology, the method includes: acquiring multiple sets of driving parameters and multiple sets of energy consumption input parameters of a vehicle operating under target conditions using a hybrid power system within a target test duration; calculating the output energy of the hybrid power system within the target test duration based on the multiple sets of driving parameters; calculating the input energy of the hybrid power system within the target test duration based on the multiple sets of energy consumption input parameters; and determining the combined hybrid power system efficiency under the target conditions based on the output energy and input energy. This method focuses on the actual operating conditions of the vehicle, and by acquiring multiple sets of driving parameters and energy consumption input parameters, calculating the output energy and input energy step by step, and determining the combined hybrid power efficiency, it can accurately reflect the actual energy efficiency level of the hybrid power system, improving the accuracy and reliability of the test results.

The application discloses a kind of multi-density source three-dimensional density inversion method, electronic equipment and storage medium, belong to geophysical exploration and artificial intelligence cross technical field, aim at solving the problem that existing gravity inversion method is difficult to effectively separate and simultaneously invert underground multiple density sources.The method generates training samples through spatial partition strategy;Construct the UNet++ deep learning network of multiple channel output, so that its output channel corresponds to different density sources;Independent normalization strategy is used to process training label to ensure network training stability;Finally, through the trained model, efficient, synchronous inversion of multiple density sources is realized.The present application avoids the error accumulation in the conventional method, greatly improves the inversion accuracy and calculation efficiency, and provides effective spatial constraints for multi-density source separation.