30 results about "Particle classification" patented technology

The embodiment of the invention discloses a biological aerosol real-time identification method and system based on ultraviolet laser induced multi-dimensional optical fingerprints. The method comprises the following steps: irradiating biological aerosol particles with ultraviolet laser with a set wavelength, scattering the ultraviolet laser under the action of the particles, and exciting the particles to generate fluorescence; mie scattering signals are collected in the first optical channel and used for measuring physical size information of particles; a broadband fluorescence signal is collected in a second optical channel and is used for determining chemical composition information of the particles; a fluorescence signal of a selected wavelength characteristic peak value is collected in a third optical channel and is used for determining microenvironment and conformation information of particles; the horizontal component intensity and the vertical component intensity of the fluorescence signal with the set wavelength are collected in a fourth optical channel and are used for determining the structural order information of the particles; based on the obtained physical size information, chemical composition information, microenvironment and conformation information and structural orderliness information, particle classification information in the biological aerosol is analyzed and determined.

The invention provides a tailing particle classification method based on multi-dimensional morphological feature compression and clustering, and belongs to the technical field of geotechnical engineering, mineral processing and particle classification, and the method comprises the following steps: S1-S6, obtaining an STL file database of tailing particles; s7, calculating and extracting morphological parameters to form an original parameter data set; s8-S10: obtaining a three-dimensional morphological parameter set which retains the original morphological feature information; s11-S12, determining an optimal clustering number by adopting an elbow rule, a contour coefficient and Gap statistics, and performing particle morphology clustering grouping on the three-dimensional morphology parameters by adopting a K-means algorithm; and S13, carrying out stacking test simulation on the clustered particle categories by using a discrete element method, analyzing the stacking density and the average coordination number of different categories of particles, and verifying the physical significance of a classification result. The tailing particle classification method can systematically and efficiently integrate particle multi-dimensional form information, can directly associate the information with macroscopic physical and mechanical properties, and is data-driven, clear in mechanism and explainable in result.

The invention relates to a particle micro-explosion combustion event detection and particle classification method based on multi-factor scoring, which takes a trajectory splitting event as a core, combines and analyzes a'termination trajectory 'and a'new trajectory' as a candidate condition of a primary micro-explosion combustion event, and converts a tracking problem from position tracking into identification and judgment of a physical process. Tracking errors caused by accidental factors in the particle tracking process can be reduced; through a two-step screening strategy of'deterministic screening 'and'probabilistic screening', the inherent'velocity attribute 'of the sub-particles can be effectively combined, a group determined as the sub-particles is firstly screened out, and the calculated amount of subsequent'probabilistic screening' is reduced; furthermore, a comprehensive scoring model is established, the micro-explosion characteristics of the mother particles are associated with the group characteristics of the child particles, a'probabilities screening 'strategy based on multi-dimensional associated information analysis is formed, the accuracy of particle classification is effectively improved, and high-precision and high-robustness recognition of the metal particle micro-explosion combustion event in the motion state is achieved.

A system comprises a particle sensor for a vehicle comprising a light source that directs a beam to an interrogation region outside the vehicle; and an optical detector that receives scattered or reflected light from an aerosol particle in the interrogation region. A processor performs a method for particle classification comprising measuring an optical response from a single particle in the interrogation region by identifying an optical response peak for scattered or reflected light from the interrogation region; measuring an amplitude and duration of the optical response peak; correcting the duration of the optical response peak based on a vehicle airspeed; based on the measured amplitude of the optical response peak, generating an expected optical response duration for water droplet using a calibration table; analyzing the measured optical response duration and the expected optical response duration to determine particle classification; and generating classification probabilities for one or more particle types.

Aspects of the present disclosure include methods for adjusting a particle classification index in response to one or more saturated data signals based on detected light from particles in a fluid stream. Methods according to certain embodiments include detecting light from particles in a fluid stream, generating a plurality of data signals based on the detected light, identifying one or more saturated data signals, generating a saturation signal index corresponding to the identified saturated data signals, and applying the saturation signal index to a particle classification index to generate an adjusted particle classification index. In some embodiments, the methods include determining one or more parameters of a particle (e.g., for a particle sorting decision) by calculating an adjusted spectral unmixing matrix for fluorescence of the particle that excludes the one or more saturated data signals. Systems and integrated circuit devices (e.g., field programmable gate arrays) for implementing the subject methods are also provided. Non-transitory computer readable storage media are also described.

This invention provides a tailings particle classification method based on multi-dimensional morphological feature compression and clustering, belonging to the fields of geotechnical engineering, mineral processing, and particle classification technology. The method includes: S1-S6: acquiring an STL file database of tailings particles; S7: calculating and extracting morphological parameters to form an original parameter dataset; S8-S10: obtaining a three-dimensional morphological parameter set retaining the original morphological feature information; S11-S12: determining the optimal number of clusters using the elbow rule, silhouette coefficient, and Gap statistic, and using the K-means algorithm to cluster the three-dimensional morphological parameters into particle morphology groups; S13: using the discrete element method to conduct a stacking test simulation on the clustered particle categories, analyzing the stacking density and average coordination number of different particle categories, and verifying the physical meaning of the classification results. This tailings particle classification method can systematically and efficiently integrate multi-dimensional particle morphological information and directly correlate this information with macroscopic physical and mechanical properties. It is data-driven, has a clear mechanism, and the results are interpretable.

"ENHANCEMENT APPLIED TO THE STATIC MACHINE ELEMENT DISCHARGE CHAMBER INTEGRATED INTO A DEVICE KNOWN AS A HYDROCYCLONE USED IN THE STAGE OF VERIFYING ORE PARTICLE SIZE QUALITY AFTER THE CRUSHING STAGE, AND ITS ASSEMBLY, COUPLING, AND DECOUPLING PROCESSES OF THE HYDROCYCLONE BODY" represented by an evolutionary solution in the industrial and commercial sector of particle classification / separation equipment for ores that have undergone concentration, desliming, solids recovery, and closed grinding circuit procedures, with particular application in equipment known as hydrocyclones (Eq), wherein, in order to solve problems of premature wear and complex maintenance of the static machine element discharge chamber, the invention is embodied in an enhancement to its constructive concept, such that it becomes composed of three components: a lower part (10), a male quick-coupling part (20), and a fixed base (30). A concept of quick engagement / disengagement of the operational lower part (10) is introduced for verification of the integrity of the cut diameter (H), being distinguished by the fact that it is sufficient to perform the quick disengagement / engagement of the assembly comprising the lower part (10) and the male quick-fit coupling part (20)."

The invention belongs to the related technical field of phase segmentation, and discloses a decoupling type phase segmentation method and system for a composite gel system microstructure, and the method comprises the steps: (1) forming a reusable particle database through particle images corresponding to each kind of target gel particles; (2) taking an artificially marked pure cement paste BSE image as a background image, inserting particles in a reusable particle database into a cement matrix area to obtain a stage 1 model training data set, and training a stage 1 universal segmentation model; (3) training the stage 2 particle classification model by adopting particles in the reusable particle database; (4) inputting the BSE image to be subjected to phase splitting into the stage 1 universal segmentation model to obtain a matrix, a pore and a particle mask; and stage 2, the classification model performs joint classification based on the particle masks and all particles in the BSE image to be subjected to phase splitting so as to obtain the specific phase category of each particle. According to the invention, the universality is improved.

This invention discloses a visual recognition-based method and system for slag particle grading, relating to the field of industrial machine vision technology. The method includes constructing a doubling sequence based on an effective scale set, calculating a snapshot matrix and performing singular value decomposition to obtain a left singular vector matrix, calculating the projection residual, determining the fusion window length and extracting the window fusion diameter, calculating a consistency flag, setting the endpoints of the grading interval, calculating the fusion score by fitting the diameter and filtering it to obtain the output grade. By introducing a multi-channel adaptive subtraction mechanism based on background statistics, the interference of slag transport background and illumination fluctuations on particle boundary extraction is reduced, improving the reliability of particle size measurement. By constructing a multi-scale particle size fusion and consistency judgment mechanism constrained by imaging blur, stable estimation and fine grading of slag particle size are achieved, improving the continuity and controllability of the grading results within the critical particle size range.

The utility model discloses magnetic density separation equipment for classifying electronic waste particles, which relates to the technical field of electronic waste treatment and comprises a bottom plate, and four supporting legs and two supporting plates are fixedly connected to the upper surface of the bottom plate. Superconducting magnets fixed to the supporting legs at the corresponding positions and a plurality of rotating rods rotationally connected with the supporting plates at the corresponding positions are arranged above the bottom plate. According to the device, various metals mixed in electronic wastes can be effectively separated at the same time through the paramagnetism of the magnetic fluid and the density difference of different metals, compared with other traditional schemes, the process steps are greatly shortened, and compared with a traditional method depending on chemical reagents or high-temperature smelting, physical separation does not need to use strong acid, cyanide or high temperature, and the cost is reduced. Separation is achieved only through physical fields such as a magnetic field and water flow, so that emission of toxic waste water and waste gas is remarkably reduced, the environmental protection treatment cost of enterprises is reduced, and meanwhile workers are prevented from making direct contact with heavy metal and harmful chemical substances.

The application discloses a method for identifying a leading particle in mud flow dynamic simulation and a method for querying a neighborhood particle. In view of the defect that the existing SPH model of mud flow cannot distinguish the leading particle, two schemes are provided under a single concept. The method for identifying the leading particle in mud flow dynamic simulation configures a 3D-CNN classification model in the SPH model to identify the leading particle and non-leading particle from the grid scale. The classification model is based on the frame data of the material model, defines a local aggregation space around the object particle, extracts a feature tensor, combines the object particle attribute, analyzes the particle attribute statistical index from the grid scale and the grid scale leading particle identification rule, generates a sample and trains the model. The method for querying the neighborhood particle in mud flow dynamic simulation is based on the leading particle identification technology, adopts tree storage for the leading particle, adopts hash storage for the opposite, obtains a global partition fusion hash table and optimizes the particle query. The application solves the problem of the leading particle of mud flow and global simulation, thereby reducing the operation complexity, optimizing the time and space cost, balancing the accuracy and efficiency of the numerical model.

The invention discloses a particle classification and size measurement method and device based on interference imaging and improved UNet + + +, and belongs to the field of particle measurement and image processing. The method comprises the following steps: acquiring a spherical particle interference fringe pattern and an irregular particle interference speckle pattern, and constructing a data set containing a mask pattern and a category label; dividing into a training set, a verification set and a test set; the training set is used for training a pre-constructed neural network model, the pre-constructed neural network model takes an original UNet + + + network as a basis, a space and channel attention module, a frequency domain dynamic convolution unit and a multi-axis Hadamard convolution attention module are introduced, and the network is trained to output a reconstructed image and a category label at the same time; and inputting a to-be-measured particle image into the trained network, outputting a particle category and a reconstructed image, and measuring the reconstructed image to obtain size information. According to the method, high-precision, real-time and end-to-end prediction of particle types and sizes is realized, and the method is suitable for a mixed scene of spherical droplets and irregular ice crystals in cloud.

The application discloses a barley seed fine-grained classification method and system, electronic equipment and a storage medium, and belongs to the technical field of intelligent detection of agricultural seeds. The application constructs an LRSG-DenseNet classification model, takes DenseNet161 as a backbone, embeds a learnable radial spectrum gating module after a DenseBlock, maps spatial features to a frequency domain through two-dimensional real fast Fourier transform, constructs a pseudo-radial frequency index, realizes amplitude spectrum adaptive re-labeling by using a learnable spectrum parameter matrix, generates a channel attention vector by global average pooling and a multilayer perceptron, obtains spatial-frequency domain collaborative features by residual fusion, and completes fine-grained classification. The application only uses ordinary RGB images, does not need a hyperspectral device, can significantly improve the perception ability of subtle features such as seed coat texture and furrows, suppresses noise interference, improves the recognition accuracy and robustness of similar barley varieties, and is suitable for seed purity detection and high-throughput nondestructive sorting.

This utility model relates to the field of metallurgical auxiliary material production technology, specifically a particle classifier for metallurgical auxiliary material production. It includes a base plate, a classifying cylinder above the base plate, an upper screen plate and a lower screen plate installed on the inner wall of the classifying cylinder for particle classification, and two discharge troughs on the side wall of the classifying cylinder for unloading. It also includes a tilting mechanism, disposed on the top surface of the base plate, for tilting the classifying cylinder. The tilting mechanism includes a frame fixed to the top surface of the base plate, two rotating rollers fixed to the outer wall of the classifying cylinder and rotatably connected to two vertical ends of the frame, and a rotating rod coaxially fixed to the surface of one of the rotating rollers away from the classifying cylinder. This utility model solves the problem of inconvenient particle classification and unloading in metallurgical auxiliary materials by setting up a base plate, a classifying cylinder, a feed funnel, a discharge pipe, a solenoid valve, an upper screen plate, a lower screen plate, a tilting mechanism, and a shielding mechanism.

The present invention provides a classification model generation method, a particle classification method, a computer program, and an information processing device which are for classifying particles using, as training data, waveform data representing a specified morphological feature and waveform data representing an unspecified morphological feature.　The present invention generates a classification model that outputs determination information indicating whether or not particles have a specified morphological feature when waveform data is input, by learning using training data including: first waveform data representing a morphological feature of particles obtained by emitting light to particles which are included in a first sample and which have the specified morphological feature; information indicating that the first waveform data has been obtained from the particles which are included in the first sample; second waveform data representing a morphological feature of unspecified particles which are included in a second sample; information indicating that the second waveform data has been obtained from the particles which are included in the second sample; and the positive rate which is the proportion of particles having the specified morphological feature.

The application discloses a kind of based on MSCO-PSO's ultra-high frequency radar array amplitude phase error calibration method, including obtaining radar echo data, the covariance matrix of array data is constructed, to obtain the coarse measurement of angle of arrival, then utilize MSCO-PSO algorithm to estimate the phase error and amplitude error of each channel, to calibrate steering vector, estimate the angle of arrival after calibration, until meet the requirement, output the estimated value of amplitude and phase error.The MSCO-PSO algorithm of the scheme is based on the framework of traditional PSO algorithm, by introducing Latin hypercube sampling for population initialization, combined with adaptive particle classification strategy, improved speed update mechanism and Gaussian random disturbance, enhance the global search ability and convergence stability of algorithm in high-dimensional channel amplitude phase error parameter space, can effectively estimate array channel amplitude phase error, restore array directional diagram characteristics, significantly improve the angle measurement precision and radial flow field space consistency, provide reliable array angle measurement basis for subsequent section surface flow velocity inversion.

The utility model provides a rotary film grader with spiral trays, which comprises a rotary frame used for being connected with a rotating mechanism, and the rotary frame is provided with one or more identical spiral trays which are arranged obliquely downwards along the circumferential direction; a feeding box is arranged on the width side of the inner side of the top of the spiral tray, and collecting channels are arranged on the outer side and the bottom of the spiral tray. By the adoption of the technical scheme, the spiral tray which is obliquely arranged and installed on the rotating frame is utilized, feeding suspension liquid flows in the length direction of the tray to generate a layer of thin film, meanwhile, centrifugal force is overlaid, the separation time can be prolonged, particles can be better layered, and the particle grading quality is improved; therefore, particles with small diameters or low density are layered.

This utility model relates to the field of dry granulation technology and discloses a drying device for a dry granulator, including a support frame. A granulation device is mounted on the top of the support frame. The input end of the granulation device is connected to the outside for feeding in the material to be granulated. The output end of the granulation device is connected to a drying mechanism for drying and classifying the material particles. The granulation device includes an inlet, the bottom of which is connected to a granulation chamber. A granulation assembly is installed inside the granulation chamber, and a discharge receiving plate is installed at the output end of the granulation chamber. In this utility model, the direct connection between the granulation device and the drying mechanism eliminates the intermediate step of "separately transferring granulated material to the drying equipment" found in traditional equipment. Simultaneously, particle classification is completed during the drying process, eliminating the need for additional classification equipment. The entire process, from raw material input to classified particle output, is seamlessly integrated, improving production efficiency.

This utility model discloses a train braking interface and a device for the graded collection and detection of wheel-rail friction particles, including a train braking test bench, a discharge circuit, a braking system, an isolation chamber, a particle grading and filtration collection device, a filter screen, and a particle settling collector. The braking system is placed in the isolation chamber, which is connected to the particle grading and filtration collection device through the discharge circuit. The particle grading and filtration collection device has multiple layers of filter screens and a particle settling collector arranged sequentially from top to bottom. A dynamic detection opening is provided on the side, connected to an external particle concentration detection device. By monitoring the changes in particle concentration within the device in real time, the operating state of the filter screen is dynamically adjusted to effectively prevent clogging. This utility model is applicable to particle detection and particle size classification collection under various brake disc materials, brake pad installation methods, and complex braking conditions. It can detect particles generated by the braking system and wheel-rail friction separately or simultaneously, providing technical support for research on environmental protection and performance optimization of train braking systems.

The invention discloses a solid-containing oil-gas-water conveying calculation method, and belongs to the field of petroleum engineering. The invention aims to solve the problem that a traditional calculation method is difficult to comprehensively consider respective physical characteristics of oil-gas-water three phases, complex interphase interface action and influence of particles on overall flow. The method comprises the following steps: step 1, determining large-scale analytic particles and small-scale semi-analytic particles in the multiphase flow according to a preset fluid domain grid size; 2, constructing a multiphase flow-particle coupling numerical model based on a particle classification result in the step 1, and distinguishing a control equation of a fluid phase and two types of particle phases; 3, configuring relevant parameters of fluid attributes and particle parameters based on the coupling numerical model; 4, on the basis of the relevant parameters, independent solution and inter-phase coupling effect calculation of the fluid field and the particle field are achieved through a two-way coupling algorithm, and calculation result data are obtained; and 5, outputting a simulation result based on the calculation result data.

A method for evaluating solid drilling fluid additives such as lost cuttings materials (LCM) includes acquiring a calibrated digital image of solid particles separated from drilling fluid circulating in a wellbore. The calibrated digital image is processed to identify individual ones of the solid particles depicted in the image. Color features and / or texture features are extracted from the identified solid particles depicted in the image. The extracted color and / or texture features are processed to identify LCM particles among the identified solid particles and to classify each of the identified LCM particles into one of a plurality of LCM classes and thereby obtain an LCM particle classification.

The invention discloses a fluidized bed catalyst and a preparation method and application thereof.The method comprises the steps that S1, a carrier solution used by the catalyst enters a reactor in a vaporific dispersion state, active components of the catalyst enter the reactor after being entrained and mixed by gas in a liquid form, and the reactor rotates around the central axis; keeping the carrier particles suspended in the reactor; s2, enabling the carrier and the active component to react in a reactor, wherein the reaction temperature is 100-450 DEG C; s3, the reactants enter a particle grading system, and a catalyst precursor with the particle size range of 10-150 microns is obtained; and S4, roasting the catalyst precursor at the roasting temperature of 450-700 DEG C for 1-12 hours to obtain the fluidized bed catalyst prepared by the rotating vapor deposition method. The catalyst has the characteristics of high activity, high strength, controllable particle size distribution and long service life, and can improve the yield of unsaturated nitrile or unsaturated amine when being applied to the reaction process of preparing unsaturated nitrile or unsaturated amine through gas-phase oxidation of unsaturated hydrocarbon.

The application relates to a transformer oil pollution degree evaluation method based on lens-free imaging and machine learning, relates to the technical field of transformer oil detection, and is used for solving the problems that the existing transformer oil particle pollution detection technology cannot obtain particle three-dimensional morphology and the detection result lacks quantitative correlation with the pollution degree. The application comprises the following steps: S1, acquiring a lens-free diffraction image of particles in a transformer oil sample to be detected; S2, amplitude reconstruction of the diffraction image; S3, phase reconstruction and thickness inversion of the diffraction image; S4, particle region segmentation and extraction; S5, structured feature extraction; S6, particle classification; and S7, pollution degree grade evaluation. The application realizes synchronous acquisition of particle two-dimensional morphology and three-dimensional thickness information by acquiring particle diffraction information in a lens-free imaging mode, combining amplitude reconstruction and thickness inversion, realizes automatic identification of particle types by combining structured feature extraction with a machine learning model, and realizes intelligent and quantitative evaluation of the pollution degree grade of transformer oil.

Disclosed herein are machine learning-based particle classification systems, and related methods, computing devices, and computer-readable media. For example, in some embodiments, a particle classification system can include an electronic processing device configured to: receive, from an imaging flow cytometer instrument, a test set comprising unlabeled data to be classified; pool the test set and a training set into a concatenated data set comprising a plurality of parameters, wherein the training set comprises labeled data; normalize the concatenated data set by making each parameter in the plurality of parameters variance one; non-linearly reduce a dimensionality of the normalized concatenated data set to a reduced dimensionality space; compute a classification parameter by classifying the unlabeled data from the reduced dimensionality space using the labeled data from the reduced dimensionality space; and provide the classification parameter for further processing.

The present application relates to a kind of environmental monitoring devices with particle collection function, belong to environmental monitoring technical field.It includes ontology, the middle of ontology is provided with collection box, the collection box top is provided with ventilation duct, the collection box bottom is fixedly connected with supporting leg, the ventilation duct inside is provided with particulate matter concentration monitoring assembly, the collection box inside is provided with three different aperture filter plate, three the fan of filter plate bottom is installed, the filter plate both sides are provided with mobile collection assembly, the mobile collection assembly is provided with sealing protection assembly, the upper surface of each mobile collection assembly is provided with cleaning assembly.The present application is integrated particulate matter concentration monitoring, hierarchical collection, sealing protection and cleaning regeneration multifunction module, constructs integrated environmental monitoring device, solves the problem of traditional monitoring device function single, particle classification precision is low, easy to block and high maintenance cost, greatly improves the practicability and long-acting property of device.

The invention discloses a tap identification method and a neighborhood particle query method for dynamic simulation of debris flow. Aiming at the defect that the existing debris flow SPH model cannot distinguish the faucet, two schemes under a single concept are provided. According to the debris flow dynamic simulation faucet identification method, a 3D-CNN classification model is configured in an SPH model, and faucet and non-faucet particles are identified from the grid scale. According to the classification model, based on material model frame data, a local aggregation space is defined around object particles, feature tensors are extracted, object particle attributes are combined, and analysis and sample generation are performed under grid scale particle attribute statistical indexes and grid scale faucet judgment rules, and the model is trained. According to the neighborhood particle query method for debris flow dynamic simulation, based on a faucet recognition technology, tree storage is adopted for faucet particles, otherwise, hash storage is adopted, a global partition fusion hash table is obtained, and particle query is optimized. The problem of debris flow faucet and global simulation is solved, so that the computational complexity is reduced, the space-time overhead is optimized, and the accuracy and efficiency of a numerical model are balanced.

The application discloses a dolomite opening and using multi-particle size classification and screening box body, which comprises a base and a box body, an adjusting assembly is arranged between the base and the box body, lateral plates are fixedly connected to the two sides of the box body, a feeding box is fixedly connected to the top of the box body, a plurality of screening plates are arranged in the box body from top to bottom, screening assemblies are arranged on the two sides of the screening plates, and buffer assemblies are arranged at the bottom of the screening plates; the application relates to the technical field of dolomite particle screening. The white dolomite opening and using multi-particle size classification and screening box body is provided with overall aperture adjusting mechanisms and single aperture adjusting mechanisms, the aperture size of a single screening plate can be adjusted, and the single aperture adjusting mechanism is suitable for a scene in which a specific particle size range needs to be accurately controlled; meanwhile, the aperture sizes of a plurality of screening plates can be adjusted, and the single aperture adjusting mechanism is suitable for a scene in which materials with different particle size ranges need to pass through, and greater flexibility is provided.

Aspects of the present disclosure include methods for adjusting a particle classification index in response to one or more saturated data signals from light detected from particles in a flow stream. Methods according to certain embodiments include detecting light from particles in a flow stream; generating a plurality of data signals from the detected light; identifying one or more saturated data signals; generating a saturated signal index that corresponds to the identified saturated data signals; and applying the saturated signal index to a particle classification index to generate an adjusted particle classification index. In some embodiments, methods include determining one or more parameters of a particle (e.g., for use in a particle sort decision) by calculating an adjusted spectral unmixing matrix for the fluorescence of the particle that excludes one or more saturated data signals. Systems and integrated circuit devices (e.g., a field programmable gate array) for practicing the subject methods are also provided. Non-transitory computer readable storage mediums are also described.

The invention belongs to the technical field of water pump design, and relates to an optimal design method of a multi-scale particle solid-liquid two-phase flow vane pump, which comprises the following steps: S1, particle grading and quasi-fluid physical property correction: correcting the density and viscosity of a quasi-fluid according to the volume fraction of fine particles; s2, axial surface flow channel design based on the particle size is carried out, so that the width of the narrow position of the flow channel meets the preset minimum passing ability constraint condition based on coarse particle physical characteristics; s3, solving a quasi-fluid axial surface flow field, calculating speed distribution and pressure distribution of the quasi-fluid in an impeller axial surface flow channel by adopting a unitary flow theory, and obtaining axial surface flow field characteristic parameters; and S4, carrying out coarse particle swarm distribution and dynamics prediction. According to the optimization design method for the solid-liquid two-phase flow vane pump with the multi-scale particles, the problems that an existing method is inaccurate in prediction, blind in design, frequent in operation faults and the like can be solved.

Methods and systems for particle classification are provided herein. A plurality of images of fluids / samples of pharmaceutical products, drugs, medicines, etc., may be analyzed using one or more machine learning models. The one or more machine learning models may comprise, for example, a convolution neural network. The one or more machine learning models may identify and classify one or more sub-visible particles in each image. The one or more one or more sub-visible particles may comprise, for example, cells, pathogens, protein aggregates, silicon oil droplets, fibers, air bubbles, glass particles, a combination thereof, and / or the like.