482 results about "Sample pool" patented technology

The embodiment of the invention provides a safety monitoring method based on mixing of a large model and a neural network algorithm, and the method comprises the steps: carrying out the real-time detection through deploying a lightweight convolutional neural network at an edge end, and screening a to-be-rechecked sample through multi-scale feature extraction and a confidence scoring mechanism; a deep neural network model is deployed at a cloud end to execute fine analysis, deep semantic features are extracted by using a residual structure and an attention mechanism, and a re-check decision is performed based on feature sensitivity and a region weight. Meanwhile, a double-cache structure is constructed to maintain a training sample pool, differential updating strategies are achieved according to sample importance, model performance is continuously optimized through knowledge distillation and comparative learning, edge computing resource constraints and detection precision requirements are effectively balanced, and intelligent upgrading of a safety monitoring system is achieved; according to the method, the detection accuracy and the real-time response capability of the system can be improved by constructing an intelligent framework of edge cloud collaboration and combining the advantages of a large model and a lightweight neural network.

The invention discloses a traceable cavity ring-down spectrometer system with a self-calibration function, and is applied to the technical field of gas concentration detection. The device comprises an optical cavity ring-down main body, a dynamic generation module and a reference analysis module which are connected in sequence, wherein the optical cavity ring-down main body comprises a laser light source, a beam splitter, an optical fiber isolator, a lens, a first reflecting lens, a first aluminum ring, a sample cell, a second aluminum ring, a cross shaft type universal coupling control device and a second reflecting lens which are arranged along a laser light path; the dynamic generation module comprises a high-purity gas inlet, a gas purifier, a dynamic generation device, a standard gas outlet and a three-way electromagnetic valve which are arranged along a gas path; the reference analysis module comprises an instrument standard gas inlet, a reference analyzer and a standard gas storage box which are arranged along the gas path. According to the method, the influence of instrument drift on analysis data caused by the use environment and instrument part loss of the online instrument is avoided, and the accuracy and the quantity value traceability of the instrument analysis data are guaranteed.

The invention discloses an industry instruction fine tuning data set automatic generation method and system, and belongs to the technical field of artificial intelligence, S10: selecting industry standard document data, and analyzing the data into a plurality of paragraph texts as data sources according to chapters; s20, generating different types of questions for the paragraph text by using a large language model; s30, scoring the generated questions according to a scoring rule, and filtering the questions with scores lower than a preset threshold value; s40, the solvability and difficulty of the question are adjusted through a large language model, and the quality of the question is optimized, and S50, a first version answer of the question is generated by using the large language model, and the answer is optimized by using a hierarchical region optimization search algorithm; s60, constructing a sample pool through global and local selection methods, calculating a sample compression ratio and screening a data set; the method has the beneficial effects that through a highly automatic process and an optimization algorithm, the data set generation efficiency and quality are improved, the labeling cost is reduced, and support is provided for rapid deployment of a large language model in industry application.

The invention relates to the technical field of laser particle size analyzers, in particular to a laser particle size analyzer with a double-ultrasonic structure, which comprises a particle size analyzer body, a sample pool is arranged on the particle size analyzer body, a sample dispersion mechanism for crushing particle blocks is arranged in the sample pool, and a stirring auxiliary mechanism for preventing suspended particles from settling is arranged above the sample pool. The stirring auxiliary mechanism can push the particles to a sound field strong area of the sample dispersion mechanism so as to improve the dispersion efficiency, and a splash-proof covering mechanism is arranged at the upper end of the sample pool. By arranging the sample dispersion mechanism and adopting a mode of combining phase orthogonality and material orthogonality layout, a stable three-dimensional acoustic vortex can be formed in the sample pool, and the coverage range of the ultrasonic cavitation effect can be obviously improved, so that the particle dispersion uniformity is improved, and the particle dispersion efficiency is improved. A detachable low-frequency ultrasonic vibrator is adopted at the bottom, so that the penetrating power of a high-concentration and high-viscosity sample is further enhanced, and dispersion dead angles can be effectively eliminated.

A method for detecting circularity of silicon carbide micro powder relates to the technical field of silicon carbide detection, and comprises the following steps: S1, turning on a flowing particle imager, preheating and then carrying out background detection; s2, adding standard particles into the sample pool, and enabling the flowing particle imager to perform automatic focusing; s3, adding the prepared silicon carbide micro powder sample into a sample pool, and detecting by using a flowing particle imager; and S4, storing the detection result, checking the silicon carbide micro powder particle image, and recording the detection result. The preparation of the silicon carbide micro-powder sample comprises the following steps: S31, adding the dried silicon carbide sample into a dry and clean beaker; s32, sequentially adding an impregnating compound, a dispersing agent and deionized water into the beaker; s33, carrying out ultrasonic dispersion; and S34, taking out the uniformly mixed sample. The method can solve the technical problem of detection failure caused by easy agglomeration of the silicon carbide sample when the flowing particle imager is used for detecting the circularity of the silicon carbide micro powder.

The invention discloses a dual-lens laser particle analyzer with an automatic calibration function. The dual-lens laser particle analyzer comprises a lead-in assembly, a shaking assembly and a mist brushing assembly, the introduction assembly is used for mixing and introducing to-be-measured particles and a water source, the shaking assembly is used for continuously shaking the particles of a to-be-measured mixed solution so as to ensure that the particles are uniformly dispersed, and the mist brushing assembly is used for brushing away mist which is generated on a lens due to too high humidity and interferes with the laser measurement degree. Through the combination of the first fixing plate, the first fixing shaft, the fixing ring and the torsional spring, the first sample pool can flexibly shake within a certain range, the motor drives the reciprocating lead screw to drive the toothed plate to move, the first sample pool can shake and swing, and through the continuous and diverse movement mode, the detection accuracy is improved. The uniform distribution of the particles in the solution is ensured, the interference of particle precipitation or aggregation on the measurement result is avoided, and the accuracy of measuring the particle size by the laser particle analyzer is obviously improved.

The invention discloses a detection device and a detection method for dissolved gas in insulating oil. The detection device comprises a rapid degassing module and an analysis module, the rapid degassing module comprises a sample bottle, a heating oil sample pool, a stirring structure, a high-purity gas storage tank and a cold trap; the sample bottle is used for injecting an insulating oil sample to be detected; the heating oil sample pool is used for heating the insulating oil sample in the sample bottle; the stirring structure is used for stirring the insulating oil sample in the sample bottle; the high-purity gas storage tank is used for introducing inert gas into the sample bottle and accelerating discharge of dissolved gas; the cold trap is used for enriching the mixed gas discharged from the sample bottle and selectively adsorbing and enriching the target gas to be detected; and the analysis module is used for detecting the gas analyzed from the cold trap. According to the detection device disclosed by the invention, the insulating oil sample to be detected is simultaneously subjected to heating stirring, gas purging replacement and cold trap enrichment, so that the dissolved gas in the insulating oil can be quickly removed, the degassing time is shortened to be within 5min, and the degassing treatment efficiency is greatly improved.

An MIG welding parameter optimization method based on a composite heat source simulation model comprises the steps that firstly, a geometric model and a physical model for simulation are determined, then grid division work is carried out on the simulation model, reasonable boundary conditions are designed for the simulation model, and then a Gaussian heat source model and a cylinder heat source model are imported into the models; the method comprises the following steps of: setting a time step, starting a simulation process, monitoring, comparing a simulation result with an actual experiment result, verifying, and continuously and repeatedly optimizing the model until the simulation result and the actual experiment result have good consistency. A composite welding heat source model is embedded in a traditional heat source simulation model, so that real data such as the shape and the forming size of a welding pool under the welding wire filling condition can be more accurately simulated, the simulation precision is high, the data are reliable, and the size of the actual welding sample pool can be well fit.

The invention relates to a device and a method of a high-sensitivity suspension single-droplet sensor for monitoring PM2.5 pollution, belongs to the technical field of aerosol microdroplet spectrum trace analysis, and solves the problem that ppb-magnitude gas cannot be monitored in the prior art. The method comprises the following steps: pumping aerosol into a sample cell of a droplet suspension system in a single droplet form; introducing required reaction gas into the sample pool through an atmosphere control system; scattered light signals of the single liquid drops are respectively input into the dynamic signal processing system and the microdroplet imaging system; acquiring the characteristic peak displacement change of the metal ion sensing microdroplet; establishing a gas particle distribution Mie scattering inversion model based on micro-droplet dynamic response; inverting the radius of the liquid drop according to the stimulated Raman scattering / stimulated elastic scattering signal; according to the quantitative relationship of the reaction, calculating the generation of sulfate by the droplet suspension system (sensor); and judging which gas in the actual atmosphere is a main oxidation pathway for oxidizing sulfur dioxide to generate sulfate pollutants according to a calculation result.

An online soft measurement method for dioxin emission concentration in MSWI process includes: performing principal component analysis on process data according to a historical process data set of the MSWI process to obtain a drift index control limit; constructing an offline model based on FTBL, and inputting the process data and historical DXN true value data of the MSWI into an off-line model; performing principal component analysis according to online data, judging whether the online data is drift data according to the drift index control limit; if the typical sample pool is the drift data, constructing an online model based on FTBL, and inputting the process data and the drift data of the typical sample pool and output data of the incremental layer of the offline model into the online model; and determining a DXN emission concentration predicted value according to an offline calculation result and an online calculation result.

The invention relates to the technical field of soil treatment, and particularly discloses a soil micro-plastic electrostatic separation device and method.The soil micro-plastic electrostatic separation device comprises a sample pool, an electrostatic adsorption mechanism is arranged on one side of the sample pool, and the electrostatic adsorption mechanism is connected with an electrostatic discharge generator through a wire; the electrostatic adsorption mechanism comprises an adsorption plate and a connecting handle; the bottom box and the heightening frame bodies are made of wood, the height of the sample pool can be adjusted by installing the heightening frame bodies in an assembling mode, a nylon coating is arranged on the outer side of an adsorption plate and used for adsorbing micro-plastics and eradicating the risk of high-voltage electric leakage, and the height of the adsorption plate can be adjusted through an arranged supporting assembly; the particle size range of the micro-plastic covered by electrostatic adsorption is wide, compared with a traditional method, the recovery rate is remarkably improved, and the original form of the micro-plastic and the natural structure of soil are protected through normal-temperature physical separation and no reagent addition.

Disclosed in the present invention are an apparatus for measuring the refractive index of a liquid on the basis of a transmission optical path, and a measurement method therefor. The apparatus comprises a laser, a sample cell, a thermostatic bath, a photoelectric imaging device, a control unit and a PC, wherein the sample cell is light-transmissive at the front and rear, and one or both of walls of a light-transmissive face is / are of a right-trapezoidal wedge-shaped structure; the thermostatic bath can perform temperature control on a liquid sample; and laser light emitted by the laser penetrates the liquid sample in the sample cell, and finally forms an image on the photoelectric imaging device placed in a transmission direction. The method comprises: when laser light passes through a sample in a sample cell, the laser light being refracted at an interface between different media, such that the direction of an emergent optical path changes, wherein the variable of the change is related to the refractive index of the sample; the laser light forming an image on a photoelectric imaging device after penetrating the sample; and calculating the refractive index of the sample on the basis of the relationship between the position of an imaging point and the refractive index. The present invention achieves measurement of the refractive index of a liquid with an accuracy better than 0.05%.

The utility model discloses a modularized fluorescence analysis and detection device, which relates to the technical field of fluorescence analyzers and particularly comprises a first shell which is a control part, a display screen is arranged at the top of the first shell, a control mainboard is arranged in the first shell, and the modularized fluorescence analysis and detection device further comprises a second shell connected with the first shell; the second shell is a detection part, and a frame body for placing a sample cell is arranged in the second shell; a fluorescence receiving part is arranged at the top in the second shell, an irradiation part is arranged at the bottom in the second shell, the fluorescence receiving part is located above the sample cell, and the irradiation part is located below the sample cell. During use, the first electric actuator can be controlled to move by operating the keys, so that the positions of the fluorescence receiving part and the irradiation part are adjusted, and a proper distance is kept between the optical filter group at the bottom of the fluorescence receiving part and the sample cell; and the light beams emitted by the irradiation part can be driven by controlling the first electric actuator to be completely concentrated on the sample cell.

The invention discloses a soil granularity detection process which comprises the following steps: pouring an air-dried soil sample on a carrier, crushing the air-dried soil sample by using a wood stick, and sieving the crushed air-dried soil sample with the sieving aperture of 2mm to obtain a sieved soil sample; loading the sieved soil sample into a soil particle dispersing device, and physically dispersing the sieved soil sample into particles to obtain a wet soil particle sample; evaporating moisture in the obtained soil particle wet sample to dryness to obtain a soil particle dry sample; adding the obtained soil particle dry sample into a sample pool of a laser particle analyzer, setting parameters of the laser particle analyzer, and measuring the soil particle size in the sample. The method has the advantages of being simple in treatment procedure and convenient to operate, the detection efficiency is high, the whole measurement process consumes less than four hours, more importantly, the soil granularity can be accurately measured, and even acid soil can be smoothly subjected to granularity detection; according to the invention, under the condition of not using any chemical reagent, the bonding force between soil particles is effectively destroyed by using an extremely simple scheme, and the accuracy of a soil particle size measurement result is ensured.

The invention discloses a sample screening method and system based on weight clustering and dynamic interval reduction. The method comprises the following steps: generating an initial sample pool through a Monte Carlo method; based on the initial sample pool, utilizing a sample screening process to carry out multiple rounds of iterative screening to obtain a screened sample pool; the sample screening process comprises the following steps: acquiring uniformly distributed candidate samples from a current sample pool by adopting a weight clustering method; predicting an upper threshold value and a lower threshold value of a limit state function in the sample pool through an agent model by utilizing an interval reduction method; and selecting the candidate samples of which the predicted values of the limit state functions are between the upper threshold value and the lower threshold value, and updating the sample pool. According to the method, candidate samples are dynamically screened in combination with a weight clustering method, interval reduction is carried out by utilizing upper and lower thresholds of a limit state function predicted by an agent model, the screening precision is ensured, meanwhile, the scale of the candidate samples is effectively compressed, the redundant calculation amount is reduced, and efficient iterative optimization is achieved.

The invention discloses an extraction-free in-situ intelligent detection system and method for furfural in transformer oil. According to the method, the in-situ surface enhanced Raman detection method containing the liquid core optical fiber is designed, coupling of a transformer oil sample pool can be achieved by introducing the liquid core optical fiber into a detection system, oil way circulation is achieved, an analysis model of the furfural content and the furfural Raman characteristic peak in sample oil is established through a deep learning convolutional neural network, and the detection accuracy is improved. High-stability, high-sensitivity and high-speed extraction-free in-situ detection of furfural in transformer oil is realized.

The invention discloses a structural reliability updating method based on domain adaptive network integration. The method comprises the following steps: constructing Monte Carlo sample pools of a source domain and a target domain; constructing a proxy model based on deep integration and active learning as a source domain proxy model; generating migration training set sample points; constructing a domain adaptive network integration agent model as a target domain agent model; selecting fine tuning training set sample points based on a learning function; calculating a sample point limit state function value to obtain a fine tuning training set; performing partial fine tuning on the target domain agent model; performing failure probability estimation by adopting a Monte Carlo method, and judging whether the failure probability meets a convergence criterion or not; and outputting a failure probability estimation value updated based on the load variable distribution change. According to the method, updating and solving of the failure probability can be accurately and efficiently completed according to the load variable distribution change condition, the safety degree of continuous service of the structure can be quantified in real time, and therefore more scientific and economical maintenance measures and overhaul plans can be formulated.

The invention discloses a time-varying reliability analysis method based on a double-layer BP neural network, and relates to the field of time-varying structure reliability analysis, and the method comprises the steps: generating a sample pool based on a joint probability density function which affects a random design variable of a to-be-analyzed time-varying structure; calculating weights of sample points, screening part of the sample points and establishing a first training data set; an inner-layer BP neural network model is constructed and trained, the minimum value of the time-varying performance function corresponding to each sample point in the first training data set about time is calculated, and a second training set is formed; an outer-layer BP neural network model is constructed, and a second training set is adopted for training; the reliability of a time-varying structure is calculated by adopting a trained outer-layer BP neural network model, the minimum value of a time-varying performance function about time is solved through an inner-layer model, and the outer-layer model is constructed based on parameters of sample points and the minimum value solved by the inner-layer model, is used for calculating the failure probability and has good applicability. The method can be used for time-varying reliability analysis of complex structures.

The invention relates to the technical field of fluorescence detection, in particular to a three-dimensional fluorescence detection correction method based on double light sources, and the correction method comprises the following steps: obtaining double light source optical parameters, sample cell geometric parameters and original fluorescence intensity data, the geometric parameters of the sample pool comprise the length, width, height, pool wall reflection coefficient and sample attenuation coefficient of the sample pool; and constructing a light intensity distribution model in a three-dimensional space of the sample cell based on the optical parameters of the double light sources and the geometric parameters of the sample cell. According to the invention, accurate characterization of the light intensity of any spatial point in the sample cell is realized, light intensity deviation caused by various factors is quantified from the source, personalized correction is carried out on each spatial point, the influence caused by optical path difference at different positions, light source fluctuation and element error is effectively offset, and the accuracy and accuracy of the detection result are improved. The consistency of detection values of standard substances with the same concentration in different areas of the sample pool is greatly improved, and the linearity of a standard curve is remarkably optimized.

The invention relates to a desulfurization slurry sulfite monitoring system and monitoring method. The monitoring system comprises a sample extraction device, a developing pool, a detection device, a liquid storage device and a control center, a light source, a sample pool and a detector are arranged in the detection device, and light emitted by the light source penetrates through a solution in the sample pool to reach the detector; the control center is in communication connection with the detector; desulfurization slurry is added into the sample extraction device, an extracting solution in the liquid storage device is added into the sample extraction device and the color developing pool, a shielding agent, a reaction solution and a color developing solution are added into the color developing pool, and the detector transmits absorption spectrum information to the control center. Compared with the prior art, the method has the advantages that the sulfite is fixed by the extracting solution, the measurement error caused by slurry oxidation is reduced, the content of the sulfite in the slurry is measured by adopting a spectrophotometric method after the sulfite reacts with the color developing agent, a measurement result can be directly obtained, characterization through a parameter relation is not needed, the operation is convenient, and the accuracy is high. And the measurement accuracy is high.

The invention provides a reactor and a method for testing thermal decomposition parameters of explosive and powder samples under simulated working conditions. The reactor comprises a reaction bottle, a sample bottle is arranged in the reaction bottle, an inner cavity of the sample bottle is a sample pool, and the sample pool is used for containing a to-be-tested sample; a tail clamp is arranged at the bottom end of the reaction bottle, a reactor thin tube is arranged in the reaction bottle, an upper pressing column and a lower pressing column are arranged in the reactor thin tube, and a spring is arranged between the upper pressing column and the lower pressing column; and a pressure pipe of the calorimeter is sleeved outside the upper pressing column. By adopting the pressure loading design, certain pressure can be loaded on materials in the explosive and powder technological process, so that the materials are subjected to a thermal acceleration calorimetry test under the pressure, and thermal decomposition parameters of the materials under pressure and temperature coupling loading are obtained. Compared with a heat acceleration calorimetry test only carrying out temperature loading in the prior art, the pressure stress is increased, the method is closer to the actual material state in multiple process stages, and the obtained thermal decomposition parameters can be used for accurately evaluating the safety of the materials in the actual technological process.

To provide a gas analysis device and a gas analysis method which analyze a sample gas more accurately than before.SOLUTION: A gas analysis device (1) consists of: a sample cell (11) including a sample gas; a reference cell (12) including a reference gas; a light source (10) for irradiating the sample cell with light (L); and a differential pressure measurement unit (13) for measuring a difference between atmospheric pressures in the sample cell irradiated with the light and the reference cell. A gas analysis method includes measurement of the difference between the atmospheric pressures in the sample cell irradiated with the light and the reference cell.SELECTED DRAWING: Figure 1

The invention discloses a multi-dimensional spectrum detection device based on combination of a transmission surface and a scattering surface, and belongs to the technical field of spectrum detection.The device comprises a camera obscura, a light source part, a detection part and a sample pool, the detection part and the sample pool are arranged in the camera obscura, the light source part comprises a light source and an angle fixing base, and the light source is connected to the angle fixing base through an optical fiber; an achromatic collimator is arranged on the end surface of the angle fixing seat and is arranged below the sample pool; the detection part comprises an emergent light detector arranged right above the midpoint of the sample cell and a plurality of scattered light detectors arranged on the side surface of the sample cell, and the emergent light detector collects transmission light spots generated after the sample is irradiated and obtains transmission information; the scattered light detector collects scattered light spots generated after the sample is irradiated and obtains a scattered spectrum image. According to the invention, the limitation of a single detection mode is effectively reduced, the distribution characteristics of scattered light at different angles and background information of transmission light are utilized to form complementary calibration, and the spectral quantitative analysis precision of the substance component content is improved.

The invention discloses a gas detection system and method. The gas detection system comprises a sample cell used for storing target gas, the cell wall is provided with a first group of light-transmitting windows and at least one second group of light-transmitting windows, and the second group of light-transmitting windows comprises a first light-transmitting window and a second light-transmitting window which are opposite to each other; the laser transmitter is used for transmitting laser to target gas through the first group of light-transmitting windows, so that the target gas generates Raman scattering light; the light condensation structure comprises a reflecting mirror and a convex lens, and the convex lens is used for focusing Raman scattering light scattered by the target gas to the second light-transmitting window and Raman scattering light reflected by the reflecting mirror at a focusing point; the optical fiber is used for receiving and transmitting the Raman scattering light; and the spectral imaging subsystem is used for generating a Raman spectrum based on the received Raman scattering light, and the Raman spectrum is used for detecting target parameters of the target gas. According to the embodiment of the invention, the collection efficiency of the gas Raman scattering light can be improved, and the collected gas Raman scattering light can be analyzed.

The constant-temperature preheating spectrum acquisition system comprises a bottom plate, and a collimator moving assembly, a sample cell assembly, a light source, a spectrograph, a temperature control meter and a relay are mounted on the bottom plate; the collimator moving assembly comprises a linear sliding rail, a horizontal connecting plate and a U-shaped supporting plate are fixed to a sliding block of the linear sliding rail, a first vertical plate is fixedly arranged on one side plate of the U-shaped supporting plate, a second vertical plate is fixedly arranged on the other side plate of the U-shaped supporting plate, a first collimator is installed on the first vertical plate, a second collimator is installed on the second vertical plate, and the horizontal connecting plate and the U-shaped supporting plate are fixed to the linear sliding rail. An optocoupler is mounted on a middle plate of the U-shaped supporting plate; the sample pool assembly comprises a heat insulation cushion block and a liquid sample pool, the liquid sample pool is provided with a reference light hole and a plurality of sample detection light holes, each sample detection light hole is provided with an optical path adjusting sheet and a sample dish, and the liquid sample pool is provided with an optocoupler slice, a temperature sensor and a heating resistor. The utilization rate of the spectrograph and the light source is effectively improved.

The invention provides an edge real-time video analysis-oriented resource efficient continuous learning method and system. The method comprises the steps of real-time video analysis and model retraining; an online model precision reduction predictor oriented to the RoI granularity is designed; continuously collecting the precision reduction degree of the lightweight model compared with the high-precision model; then designing a double-layer mixed sample pool, and based on the designed RoI granularity-oriented online model precision reduction predictor; randomly introducing a certain proportion of RoI historical samples to relieve the problem of disastrous forgetting; and finally, developing a deep reinforcement learning DRL-based retraining progress controller. Based on the evaluation result of each round, dynamically estimating a model convergence state to determine a retraining termination opportunity; by introducing a behavioral clone BC loss function, the controller converges the optimal strategy more quickly in an offline training stage. The technical scheme of the invention has an important positive significance for improving the SP income in the MEC environment.

The invention provides an oil product near infrared spectrum detection method and a detection device.The device comprises a sample pool, a near infrared light source, a linear array type infrared light filter, a linear array camera and a circuit part, and the sample pool is used for containing an oil sample to be detected; the near-infrared light source is used for irradiating an oil sample to be detected; the linear array type infrared optical filter is a step type thin film FP cavity filter, and the step type thin film FP cavity filter is used for performing multispectral light splitting treatment on an optical signal transmitted when the near-infrared light source irradiates the oil sample to be detected to obtain a multispectral transmission signal; the linear array camera comprises a linear array type infrared image sensor and a camera circuit board, and the linear array type infrared image sensor is used for sensing the multispectral transmission signal and generating image data through the camera circuit board; and the circuit part is used for obtaining the component information of the oil sample to be detected based on the image data and the oil component inversion model, so that the function of quickly and accurately detecting the quality of the light oil on site is realized.

The invention discloses an adaptive PC-Kriging reliability analysis method and system based on active learning, and the method comprises the steps: obtaining uniformly distributed first candidate samples from a pre-generated MC sample pool through employing a weight clustering method, and constructing an initial PC-Kriging model; selecting samples by using an interval reduction method to construct a new sample pool, and dividing the new sample pool into two sub-sample pools of a security domain and an invalidation domain; aiming at the two sub-sample pools, adopting the weight clustering method again, and respectively selecting uniformly distributed second candidate samples from the two sub-sample pools; constructing a crossing point by using the second candidate samples distributed in the failure domain and the security domain; taking the constructed crossing point as a newly added experimental point, and carrying out iterative updating on the PC-Kriging model; and judging convergence and outputting a result. According to the technical scheme, through interval reduction of the focusing key area and model updating based on the crossing points, the limit state surface can be more accurately approached, the number of experimental points is reduced, the prediction precision is improved, and the calculation resource utilization is more efficient.

The invention relates to the technical field of livestock measurement, in particular to a beef cattle body size intelligent measurement device and method, in the beef cattle body size intelligent measurement device and method, hoof joint point pixel coordinates are converted and angle differences are calculated in combination with a lexicographical sequence block reordering algorithm, identity binding codes are accurately generated, uniqueness and stability of beef cattle individual recognition are ensured, and the accuracy of beef cattle body size measurement is improved. The method effectively reduces the identification confusion risk when multiple beef cattle are measured at the same time, generates a simulation sample through local pixel block rendering and generative adversarial network, screens a sample according with a threshold according to authenticity scores, improves the sample richness, optimizes the coverage of a model training set, extracts body size fields and position coding values, and calculates correlation coefficients between fields. Low-correlation field pairs are removed, only high-correlation fields are reserved for cross combination, a modeling sample pool is constructed, the representativeness of samples and variable combination diversity are enhanced, and the fitting precision and popularization capability of a body size estimation model are improved.

The invention discloses a method for measuring the molar concentration of nano-particles. The method comprises the following steps: diluting a nano-particle suspension with a high-molecular polymer solution or dispersing nano-particle powder into the high-molecular polymer solution; transferring the suspension liquid to a spacing net on the side of a sample pool in an electrophoresis tank; nano-particles migrate from the sample pool to the buffer pool under the driving of the electric field and are uniformly distributed on the spacing net in a single-particle manner; performing single-particle imaging on the spacing net by using an optical microscope; and counting the number of the nanoparticles in the image so as to measure the molar concentration of the nanoparticles in the sample. The method can be used under the condition that the properties of the nanoparticles are unknown or no standard substance exists, the detection object has no additional condition, and the accuracy is high.