59 results about "Optimal modeling" patented technology

The invention provides a scaling modeling method based on aeroelasticity constraints. The method includes the steps that (A) an aerodynamic force analytical model and a structure finite element model are established according to a standard wing model; (B) a structure flutter analytical model is established according to the aerodynamic force analytical model and the structure finite element model; (C) the flutter characteristic of the model is solved according to a boundary condition; (D) whether the flutter characteristic meets a predetermined requirement or not is judged according to a predetermined constraint condition. The scaling modeling method has the advantages that in the wind-tunnel scaling modeling method of a composite material wing, the dynamics constraint, the pitch line constraint and the flutter constraint are comprehensively considered, the flutter model is optimally obtained, and thus the dynamics and flutter characteristic of a true structure can be better simulated; by optimal modeling of a low speed and high subsonic speed flutter model, flutter scaling modeling procedures based on an optimization method are respectively established, the modeling efficiency can be improved, the model can be made to well reflect the aeroelasticity characteristic of an entity, and the method is applicable to aeroelasticity wind-tunnel modeling of civil aircraft composite material wings.

The present invention discloses a personalized commodity matching and recommending system. The system comprises a personal property acquisition module, a personal style calculation module, a personal data storage bank, a commodity attribute acquisition module, a commodity style calculation module, a commodity data storage bank, a matching and recommending module, a course module, and an output display module. Multiple dimensions of personal property data of a person is collected, a most matched type of commodity or most matched commodities combined by multiple types of commodities are recommended for a person after calculation, and an optimal modeling guide is provided for a user, so that a purpose of precision shopping is achieved, and the system has high practicability. The present invention further provides a personalized commodity recommending method.

The invention relates to a fuel cell, and discloses a proton exchange membrane fuel cell model optimizing process method. According to the technical scheme of the invention, structural parameters of a fuel cell are inquired at first, then working parameters and output voltages under different output currents of the fuel cell are measured by instruments, after the mean value of error sum of squares of the estimated output voltages of a fuel cell model and actually-measured output voltages is defined as an objective function for optimal modeling, model parameters are obtained by an optimization technique, and the model is provided for researching the proton exchange membrane fuel cell. The model obtained by the method is higher in precision and stronger in stability. When modeling is performed repeatedly for multiple times, all of the optimal value, the worst value, the mean value, and the standard deviation of the objective function values of the model are smaller, wherein the optimal value, the worst value, the mean value represent the precision of the model, and the standard deviation represents the stability of the model. When modeling is performed for the proton exchange membrane fuel cell by the method, better convergence is achieved.

The present invention discloses a method for rapidly identifying meat with different animal sources. The method is characterized by comprising: (1) placing a meat sample at a room temperature, and adopting a near-infrared spectrometer diffuse reflection manner to sample a spectrum of the meat sample after the temperature is balanced; (2) selecting spectrum data of the optimal modeling wave band, and carrying out a pre-treatment on the selected spectrum data; (3) adopting a main component analysis method to extract the spectrum feature information; (4) adopting a discrimination analysis method to establish a qualitative recognition model; and (5) calculating the mahalanobis distance of the meat sample, and judging the classification of the meat sample according to the mahalanobis distance value. According to the present invention, the disadvantages of complex operation, long detection time, high chemical reagent consumption and high cost of the existing identification method are overcome; and the method has advantages of rapidness, no loss, accuracy, stability, simple identification process, and no requirement of professionals, and is easy to promote and apply in the food industry and the food law enforcement and supervision department.

The invention belongs to the technical field of video coding, and particularly relates to a rate-distortion optimizing method based on time domain dependence in low-delay video coding. A circulating group of picture (GOP) structure is adopted, a GOP is formed by every four frames, the frames in the GOPs are allocated to different layers, and the frames in the same layer follow the similar reference frame and QP allocation rule. The time domain dependence of a hierarchical structure in the low-delay video coding is analyzed, a time domain propagation chain shown in picture 2 is established according to the time domain dependence in the low-delay coding, and then rate-distortion optimal modeling of time domain dependence is carried out. A propagation factor omega a and global lagrangian multiplier lambda g are got. Finally, according to the propagation factor omega a, time domain rate-distortion optimizing is achieved only by adjusting the global lagrangian multiplier lambda g.

The invention discloses an optimal modeling method for the maximum power energy storage of photovoltaic power generation transmission. The method comprises the following steps of establishing a mathematical model of a photovoltaic single-string array, establishing a mathematical model of a photovoltaic array under a partial shade condition and the like. The method is convenient and practical and has a good effect.

The invention provides a rapid detection method for organic matters in soil based on an infrared spectroscopic intelligent identification model. The method comprises the following steps: acquiring a soil sample; acquiring mid-infrared photoacoustic spectral information and carrying out continuous multitime scanning to obtain average spectrum; analyzing the content of organic matters in a soil sample by using a chemical process; preprocessing spectral information; acquiring mid-infrared photoacoustic spectral information of a to-be-detected soil sample; taking average spectrum and carrying out preprocessing; determining the chemical reference value of the to-be-detected soil sample; calculating mahalanobis distance between the to-be-detected soil sample and training samples and establishing a new sequence matrix according to a sequence from smaller distance to bigger distance; establishing a prediction model with different modeling numbers from the new sequence matrix according to the characteristics of the to-be-detected soil sample; and evaluating the parameters of the model, selecting an optimal modeling number and carrying out optimization so as to obtain the intelligent soil identification model. According to the invention, the intelligent identification model according with the spectral characteristics of the spectrum of each sample is established and one model corresponds to one sample, so background interference and variability effect of soil can be effectively reduced; and the intelligent identification model has good universality, is stable and effectively improves prediction accuracy.

The invention discloses a method for analyzing content of dibutyl phthalate in a mixed material through a near infrared spectrum. The method comprises the following steps: preparing and collecting a batch of samples of which the content of dibutyl phthalate is in a certain range; repeatedly collecting the spectrum of each sample for at least one time, performing sample spectrum statistic analysis and principal component analysis, and removing an abnormal spectrum; performing spectrum analysis, and selecting a spectral band for modeling; dividing multiple samples into a modeling set and an inspection set; establishing various models by combining the spectral band with a preprocessing method, and selecting optimal modeling parameters and determining the optimal model according to root-mean-square prediction errors (RMSEP), RMSEP/dimension curves and principal component (PC) analysis of the spectrum; expanding sample spectrums in a corresponding concentration range and adding the sample spectrums into the modeling set when the concentration range needing to be detected changes, and modifying the detection limit of the method; and acquiring a near infrared spectrum of a to-be-detected sample, and directly detecting the sample concentration through the model. Any solvent is not used, any standard substance does not need to be prepared, and the method has the advantages of short period, environment friendliness and the like.

The invention relates to high-flux near-infrared sensitive fast non-destructive analysis for the impurities and tensile strength of tablets, belonging to the field of analyzing pharmaceutical preparation. The high-flux near-infrared sensitive fast non-destructive analysis comprises the following steps: preparing or collecting a tablet sample; collecting the near-infrared diffusion spectroscopy (NIR-DRS) of the known tablet sample through a fourier transform near infrared spectroscope; determining the reference value of impurity content of the tablets by using high performance liquid chromatography (HPLC); determining the reference value of the tensile strength through a vernier caliper and a tablet hardness tester; preprocessing the spectrum; selecting the optimal modeling wavenumber range and rejecting singular value; respectively establishing an NIR-DRS-based impurity and tensile strength correction model and evaluating the performance of the models; collecting the NIR-DRS of the unknown tablet samples; performing preprocessing identical with that of the NIR-DRS of the known sample on the NIR-DRS of the unknown tablet sample; and predicting the impurity content and tensile strength of the unknown tablet sample by using the established models. The method is high in sensitivity, free from preprocessing of the samples, fast and non-destructive in analyzing, and accurate in results.

The invention belongs to the technical field of video coding, and particularly relates to a method for layering time domain rate-distortion optimization in random access video coding. A cyclic group of picture (GOP) structure is adopted, every eight frames form one GOP and are distributed to different layers, frames on the same layer abide by similar reference frame and QP distribution rules, as shown in figure 1, the coding sequence of frames is represented by EOC, and the playing sequence/time sequence of frames is represented by POC. Time domain dependency on the same layer and different layers among coding units in random access coding is analyzed, nested layering time domain propagation chains are established according to time domain dependency in random access coding, as shown in figure 3, and then layering time domain dependency rate-distortion optimal modeling is conducted and propagation factor kv and global lagrangian multiplier lambda g are obtained. Finally, according to the propagation factor kv, time domain rate-distortion optimization is achieved by adjusting the global lagrangian multiplier lambda g.

The invention provides an optimal modeling and operating method for a photo-thermal power station coping with high-proportion photovoltaic grid connection. The method mainly utilizes schedulability ofa heat storage system in a photo-thermal power station, translates light heat energy when sunlight is sufficient, provides required power shortage for sudden reduction of sunset photovoltaic output,and simultaneously provides climbing requirements in a rapid climbing stage of a net load power curve by utilizing good adjustment characteristics of a photo-thermal unit. The method comprises the following steps of: constructing a mathematical model of a photo-thermal power station in detail, and establishing an optimal scheduling model of the power system containing the photo-thermal power station by taking cost minimization as a target; and by utilizing the schedulability and good adjustment characteristics of the photo-thermal power station, performing optimal dispatching of the power system containing the photo-thermal power station, and smoothing the net load power curve to relieve the problem of a duck curve, promote the photovoltaic high-proportion grid-connected consumption and improve the safety stability and the economy of system operation, reduce the influence of the randomness and the intermittence of photovoltaic power generation on the safety stability of the power system.

The invention discloses an optimized modeling method of a soft measuring instrument for a magnetic chain based on a least squares support vector machine during the stable suspended movement process of a bearingless asynchronous motor. The modeling method comprises the following two parts: modeling via the least squares support vector machine and the determination of an optimal model based on the improved particle swarm optimization. A soft measuring instrument model based on the least squares support vector machine is provided for the magnetic chain variable of the bearingless asynchronous motor difficult to measure in an online way, and the defects of low control accuracy and the like caused by time lag in the traditional offline measuring method can be well overcome. The modeling method has various advantages of automatic parameter optimization, high prediction accuracy, small sample demand size, strong anti-interference capability and the like.

The invention relates to a method for quickly detecting the tablet weight of medicine tablets through near infrared spectroscopy, and belongs to the field of medicinal preparation mass control. The method includes the following steps that firstly, medicine tablet samples are collected, and the tablet weight reference value of each tablet sample is measured; secondly, optimal spectral measurement parameters are screened out, and a Fourier transformation near infrared diffuse reflection spectrum of each tablet sample is measured through a Fourier transformation near infrared spectrometer; thirdly, calibration set samples and prediction set samples are selected; fourthly, an optimal spectral data pretreatment method is selected; fifthly, an optimal modeling spectral range is screened out; sixthly, abnormal spectrums or samples are diagnosed and removed; seventhly, calibration set spectrums of the tablet samples are related with the tablet weight reference values through chemometrics technology, and a tablet weight calibration model is established; eighthly, the performance of the tablet weight calibration model is evaluated through verification set samples; ninthly, the established calibration model is applied to predicting the tablet weight of the unknown tablet samples. The method is quick, accurate and capable of being used for mass control over the tablet weight of the medicine tablets and online detection of the tablet weight of the medicine tablets.

The invention provides a configuration and capacity optimization method used for distributed energy system technology selection. The method comprises the following steps that: (1) analyzing the load of a distributed energy system; (2) analyzing the resources of a place where the distributed energy system is positioned; (3) selecting one type of technology selection; and (4) adopting a mixed integer linear programming method to meet energy balance constraints, energy production equipment constraints and energy storage equipment constraints, and calculating the configuration and the capacity of lowest annual average cost. By use of the configuration and capacity optimization method used for the distributed energy system technology selection, the mixed integer linear programming algorithm is adopted to design the distributed energy system, the synthesis solving of a continuous design variable and a discrete design variable is both given consideration, the optimal modeling of various different distributed energy technologies can be realized, the optimal modeling flexibility and the solving flexibility of the system are improved, the optimal solving problem of the large time scale is effectively solved, the solving accuracy of the design variable is guaranteed, system design objectivity and optimal result accuracy are guaranteed, and a user is assisted in realizing the design of the distributed energy system.

The invention provides a near-infrared detection method for components of HTPB (hydroxyl-terminated polybutadiene) propellant slurry. A near-infrared spectrum detection technology is adopted, the influence of characteristics of an HTPB propellant slurry sample on test accuracy is solved by selecting rotatable diffuse reflection sampling technology and multipoint acquisition technology; the relationship between absorbance and component content is established with stoichiometry by selection of analysis bands and determination of optimal modeling parameters including optimal variable number and the like; a quantitative component content method is formed by establishing an accurate correction model; quantitative results of multi-component content of a to-be-detected sample can be given simultaneously by one near-infrared spectrum of the detected sample according to the relationship between absorbance and content. Rapid detection of the content of the binder HTPB, a plasticizer DOS and an oxidizing agent AP in the propellant slurry is realized; the method has the characteristics of being fast, efficient and nondestructive in detection, the change of the component concentration of the slurry in the charge production process can be found timely, stability and uniformity of the process quality are controlled, and unnecessary waste is avoided.

The invention discloses an establishment method for an alcoholic fatty liver zebra fish model. The establishment method includes the following steps that AB-series wild zebra fishes are domesticated in a laboratory, and the healthy adult zebra fishes are picked out to be evenly divided into 16 groups at random; a central combinational design method is adopted, and the ethyl alcohol concentration, the exposure time and the test cycle are designed; after the adult zebra fishes in all the groups are fed according to design conditions, the survival rates and average lipid droplet areas of the adult zebra fishes in the groups are calculated; the obtained 16 groups of survival rates and average lipid droplet areas are subjected to standard deviation standardization, survival rate standardized values and average lipid droplet area standardized values of all the groups are added and then divided by 2 to obtain the comprehensive grades of the adult zebra fishes in the groups; the comprehensive grades of all the groups are subjected to multivariate regression analysis and binomial fitting to obtain a regression equation, and the optimal modeling condition of the alcoholic fatty liver zebra fish model is determined according to the regression equation. The establishment method is low in cost and short in cycle, the alcoholic fatty liver pathomechanism can be deeply researched, and a novel treatment means is found for the alcoholic fatty liver.

The present invention relates to a path planning method through collaboration of multiple underwater robots. The method comprises the steps of: employing a discretization method in a rotating coordinate system to perform modeling of a planning space; giving a target function and constraint conditions to perform optimal modeling of the path planning of each underwater robot; obtaining the optimal path of each underwater robot according to the planning space model and the optimal model of path planning of an underwater robot; and according to the optimal path of each underwater robot, determining the optimal index function of the multiple underwater robots in the collaborative constraint conditions to obtain the optimal path of the multiple underwater robots. The navigation path of each underwater robot is primarily planned based on the improved consistency theory to re-plan the path of each underwater robot on this basis to meet the collaborative constraint conditions, and therefore, the path planning method through collaboration of multiple underwater robots is simple and feasible, smooth in path, short in optimization time and high in efficiency.

The invention provides a financial risk prediction method and device based on automatic preferred modeling and electronic equipment. The method comprises the following steps: presetting a model configuration file, wherein the model configuration file comprises a model generation file for automatically generating each model; receiving user input, wherein the user input comprises financial product time nodes and model generation parameters; performing matching processing, anddetermininga model generation file matched with the user input ; generating a financial risk model set according to the matched model generation file, and automatically training each financial risk model; selecting a corresponding test data set, performing effect evaluation on each model in the financial risk model set,and automatically selecting an optimal financial risk model according to evaluation indexes; and predicting the financial risk of the new user by using the optimal financial risk model. According to the invention, the model can be generated more automatically, the working efficiency of business personnel is improved, and the modeling system is optimized.

The invention relates to a universality and stability verification method for a model specific to frying oil polar component detection. The method comprises the following steps: collecting samples, establishing a sample set, acquiring Raman spectra of the samples in the sample set, randomly selecting a modeling set and a prediction set, and then performing analysis modeling on the Raman spectra byvirtue of multiple algorithms; and comparing a modeling result with data corresponding to the modeling set and the prediction set, and according to a correlation coefficient and an error, judging a stability and universality priority queue of a built model, thus verification is completed. The invention provides the verification method for universality and stability of the model specific to the frying oil polar component detection, accuracy and feasibility when the verification mehtod provided by the invention is used for detecting the stability and universality of the model are provided by virtue of specific experimental data, and finally an optimal modeling method is provided by virtue of the verification method provided by the invention; and the model built by virtue of the modeling method has higher accuracy when the polar component detection is performed and can more accurately evaluate quality of frying oil.

The invention relates to a detection method suitable for a traditional Chinese medicine suspension system. The detection method comprises the steps: performing mid-infrared spectrum scanning on a traditional Chinese medicine suspension system sample, collecting spectrum data in an online mode, collecting the traditional Chinese medicine suspension system sample and obtaining test values of ingredient contents in the sample; performing model parameter optimization on the collected spectrum data and the test values; choosing an optimal modeling method according to the optimized model data to establish an optimal model; scanning a spectrum of the traditional Chinese medicine suspension system in an online mode and introducing the obtained spectrum data into the optimal model to predict ingredient contents in the traditional Chinese medicine suspension system to be tested. According to the detection method disclosed by the invention, online analysis of the traditional Chinese medicine suspension system is achieved, and ingredient contents in the traditional Chinese medicine suspension system can be accurately analyzed; the method belongs to an in-situ non-destructive technology and canquickly and safely collect analysis data in real time; a technological process, a mechanism and key control parameters can be deep known, a production technology terminal point can be judged, and convenience in operation is achieved.

The embodiment of the invention provides a task optimization method and system, and the method comprises the steps: receiving a task optimization request of a to-be-optimized task, and obtaining optimization demand data based on a task scheduling rule carried in the task optimization request; executing an optimization modeling task based on the optimization demand data, and generating an optimization task model; the target optimization result meeting the to-be-optimized task is determined based on the optimization task model, so that the to-be-optimized task is subjected to automatic modeling processing, repeated modeling according to different application requirements is not needed, the problem of consumption of a large number of computing resources is avoided, and the processing efficiency of the optimization problem can be improved.