31145results about "Material analysis" patented technology

The invention relates to an intelligent detection and evaluation method and system for underground pipeline defects, and the method comprises the following steps: S1, collecting multi-modal data of an underground pipeline based on an inspection robot, and transmitting the multi-modal data to an intelligent detection and evaluation platform; s2, the intelligent detection and evaluation platform preprocesses the collected multi-modal data; s3, performing defect detection on the underground pipeline based on the preprocessed multi-modal data; s4, constructing a three-dimensional model of the underground pipeline, and superposing a defect detection result into the three-dimensional model; and S5, based on the three-dimensional model of the underground pipeline, performing intelligent evaluation on the underground pipeline, and generating a pipeline condition evaluation report. According to the invention, the efficiency and reliability of underground pipeline defect detection are effectively improved.

The invention relates to the technical field of greenhouse gas monitoring, in particular to a greenhouse gas collaborative monitoring and analysis platform. According to the technical scheme, the system comprises a cross-modal data fusion module, a causal reasoning analysis engine, a holographic dynamic visualization system, a distributed edge computing node and a self-adaptive decision optimization module, and the cross-modal data fusion module is used for integrating satellite remote sensing data, a ground sensor network, unmanned aerial vehicle mobile monitoring data and industrial Internet of Things emission source data in real time; a dynamic weight distribution algorithm is adopted, the multi-source data fusion weight is automatically adjusted according to environmental parameters, and a space-time continuous greenhouse gas concentration field is generated; a causal reasoning analysis engine is based on a hybrid architecture. According to the method, data privacy is guaranteed and delay is reduced through multi-source data fusion, causal reasoning analysis, holographic visualization and edge calculation, and comprehensive and accurate monitoring, scientific prediction analysis, efficient decision execution and risk prevention and control of greenhouse gas are realized based on decision optimization of dynamic games and block chain smart contracts.

The invention relates to the technical field of power equipment state monitoring and the Internet of Things, in particular to an SF6 gas state intelligent diagnosis and early warning system based on the Internet of Things, which comprises a sensor array module, an edge computing module, an anti-interference communication module, a cloud platform intelligent analysis module, an early warning and linkage control module, an energy management module and a self-checking and fault-tolerant module. The sensor array collects gas state and equipment environment data in real time, and the data are stably transmitted to the cloud platform through the anti-interference communication module after being preprocessed through edge computing. The cloud platform uses a multi-modal data fusion and deep learning algorithm to realize gas leakage trend analysis, leakage source positioning and risk level evaluation; and the early warning and linkage control module triggers graded early warning according to the diagnosis result and is linked with related equipment for emergency response. According to the invention, omnibearing intelligent monitoring and management of the SF6 gas equipment are realized, the monitoring accuracy and the system reliability can be effectively improved, and the equipment fault risk is reduced.

The invention provides a particulate matter and ozone source monitoring method and system based on a distributed sensor, and relates to the technical field of pollution treatment. According to the invention, sensor nodes with geographic perception capability are deployed in a monitoring area in a high-density manner, pollutant concentration and meteorological parameters are collected in real time, and data are uploaded to a cloud platform for preprocessing and dynamic calibration; a machine learning model is constructed based on the combined features of the pollutants and the meteorological factors, a driving relation is mined, and pollutant influence factors are extracted; further performing joint modeling on the influence factors and regional emission source data, identifying the coupling strength between pollutants and emission sources by adopting a classification or clustering method, and judging the categories of main sources; backward trajectory simulation, source fingerprint analysis and multi-source regression decomposition are combined to realize pollution path inversion and source contribution rate quantification; and finally, constructing a geographic information visualization platform, displaying a pollution thermodynamic diagram, a contribution change diagram and an evolution path diagram, and providing support for multi-source pollution traceability and scientific management and control.

Provided herein are methods for capturing an analyte from a first region of interest of a biological sample on a substrate, where the biological sample comprises the first region of interest and a second region, and where the method includes contacting the second region with a sealant in order to create a hydrophobic seal thereby preventing an interaction between an analyte from the second region with a capture domain of a capture probe.

The invention discloses an atmospheric pollution diffusion path tracing method and system based on meteorological data, and relates to the technical field of pollution path tracing, and the method comprises the steps: building a high-density monitoring network; obtaining a time sequence point monitoring data set of the high-density monitoring network, and distributing pollution hot spot areas; configuring a high-precision sensor and a monitoring unmanned aerial vehicle in a pollution hot spot area, establishing three-dimensional data of a pollution field, and synchronously obtaining a wind power data set; establishing a state variable; performing observation variable-based wind power data set prediction fitting on a group of initial parameters by using an atmospheric diffusion model, generating pollutant distribution, and establishing a candidate solution; and performing global particle search on the candidate solution to establish a pollution diffusion path tracing result. The technical problems that in the prior art, pollution hot spot areas are difficult to accurately recognize, and the accuracy and reliability of atmospheric pollution diffusion path tracing are low are solved, and the technical effect of improving the accuracy and reliability of atmospheric pollution diffusion path tracing is achieved.

The invention relates to a dynamic adjust gyroscope life forecasting method based on gray model. By data collection of vibration effective value, random drift and environmental temperature parameter which are preprocessed using radial neural networks, influence of environmental temperature on vibration effective value and random drift is eliminated and random drift and effective value just related to time are obtained by subtracting drift constant value term, then trend term of vibration effective value and random drift are extracted by using wavelet transformation and gray model are built separately for their trend term. The smaller data in two values of life predicted of dynamic adjust gyroscope unless two predicted values exceeding performance parameter limitation when dynamic adjust gyroscope is considered losing effect. The invention uses performance parameter of life probative period of product to predict its life, showing discipline of performance parameter and life of dynamic adjust gyroscope. It is easy and convenient economical and reliable.

The invention belongs to the technical field of environmental science, and relates to a multi-source satellite remote sensing cooperative atmospheric pollution monitoring and dynamic tracing method and system. The method comprises the following steps: collecting, processing and screening multi-source data; constructing a pollutant diffusion model, and generating pollutant concentration distribution data; dynamically adjusting the diffusion coefficient and attenuation rate of the pollutant diffusion model, and generating a pollutant concentration distribution thermodynamic diagram; performing space matching, and iteratively updating the diffusion coefficient and the attenuation rate of the pollutant diffusion model until matching parameters meet set conditions; and verifying pollutant concentration distribution data in the pollutant concentration distribution thermodynamic diagram. According to the invention, parameters of the pollutant diffusion model can be dynamically adjusted and optimized based on real-time meteorological data, the accuracy of pollutant concentration distribution data generated by the pollutant diffusion model is ensured, and real-time tracking and accurate positioning of a pollution source are realized; the monitoring precision is obviously improved; the cooperative monitoring efficiency is improved, and comprehensive monitoring of atmospheric pollutants is realized.

The invention belongs to the technical field of automatic detection, and discloses an online laser detection and grading regulation and control system for particle size distribution of crushed raw ore. The invention aims to solve the problems of halo effect interference caused by adhesion of fine dust in a complex industrial environment and insufficient detection precision of a traditional method. The system obtains dynamic scattering light field data through the laser scanning module, identifies a halo effect region and generates a compensation coefficient by using the light field preprocessing module, and realizes accurate reconstruction of particle boundaries in combination with the edge reconstruction module. The particle size distribution analysis module extracts multi-dimensional geometric features based on the accurate boundary data, and generates a real-time particle size distribution curve. The grading strategy generation module formulates a regulation and control strategy according to deviation characteristics of the curve and a preset standard, and the dynamic regulation and control module adjusts parameters of crushing equipment in real time and optimizes particle size distribution. The method effectively overcomes the halo effect interference, improves the detection precision and the system stability, breaks through the single limitation of a traditional method, and provides comprehensive data support for a downstream process.

A coulter assembly for collecting soil samples from an agricultural field in one embodiment comprises an annular collection blade configured for penetrating soil to capture a sample, an annular cam ring configured for stationary mounting to a frame of an agricultural vehicle and comprising a cam track, a blade hub coupled to the blade for rotatably supporting the annular collection blade from the annular cam ring, and a movable sample collector mounted to the annular collection blade. The moveable sample collector is configured and operable for extracting a soil sample as the annular collection blade is rotated through the soil. The moveable sample collector in one embodiment comprises a piston mechanism including a cylinder and rod movably disposed therein which actuated by the annular cam ring to alternatingly open and close a collection end of the cylinder as the annular collection blade rotates through the soil.

The invention discloses an atmospheric environmental pollution event emergency monitoring and early warning method and system, and belongs to the technical field of environmental management. The method comprises the following steps: acquiring multi-scale meteorological field data and chlorine concentration real-time data of at least five monitoring stations, extracting a wind field direction and a concentration change rate, and determining a position and a wind direction of a potential pollution hot spot area. The information is sent to the unmanned aerial vehicle, and the unmanned aerial vehicle automatically plans a path and collects supplementary concentration data from three angles. And fusing the monitoring data and the supplementary sampling data, and combining with a meteorological field input pollutant three-dimensional concentration distribution model to generate three-dimensional distribution data. And inputting the CALPUFF reverse model, and carrying out inversion to obtain the specific position and the emission intensity of the pollution source. According to the scheme, the accuracy and timeliness of source tracing of the pollution source are improved, high spatial resolution and flexible response capability are achieved, and efficient and intelligent technical support is provided for intelligent early warning, rapid source tracing and scientific response of sudden toxic gas leakage events.

The present invention belongs to the technical field of immunology, and specifically relates to a humanized nano antibody against IL-6, and a recombinant vector and application thereof. The present invention analyzes the screened anti-IL-6 nano antibody 4G10 sequence, and for screening the heavy chain of IL-6, the anti-IL-6 nano antibody 4G10 includes a framework region FR and an antigenic determinant complementary CDR, and the CDR1, CDR2, and CDR3 regions of the screened anti-IL-6 nano antibody 4G10 sequence are transplanted into the humanized hs2dAb skeleton to obtain the humanized nano antibody 4G10 against IL-6 of the present application; the recombinant nano antibody expressed by the gene encoding the humanized nano antibody 4G10 against IL-6 and the His tag gene fusion can specifically recognize the IL-6 antigen, and can be applied to the molecular diagnosis of autoimmune diseases and tumors and the preparation of autoimmune diseases and anti-tumor drugs.

The invention relates to the technical field of image analysis, in particular to a calcium carbonate master batch wind shear forming detection system based on machine vision. A calcium carbonate master batch wind shear forming detection system based on machine vision comprises an edge pixel collection module, a boundary trend recognition module, a distortion track repair module, an internal texture analysis module and a forming anomaly detection module. According to the method, the gray difference and gradient angle of continuous pixels are calculated, an edge distribution diagram is constructed, boundary detail expression is enhanced, the pixel arrangement direction and gray change analysis are combined, continuous edge line segments are extracted, the boundary recognition precision is improved, a closed contour is repaired according to line segment connection offset, and structural integrity is ensured; the method comprises the following steps: calculating a gray change rate in an internal region block, drawing a texture fluctuation map, accurately marking a sudden change region, realizing abnormal particle identification, fusing pixel-level gray features and region structure analysis, and improving the precision and integrity of form perception and defect identification.

The present invention relates to the field of data fusion technology, and in particular to a gas sensor array data fusion method and system. The method comprises the following steps: acquiring and dividing the gas sensor array data of the monitoring area to obtain the gas sensor monitoring level data; acquiring the environmental sensor data of the monitoring area and constructing a multi-dimensional gas sensor network of the monitoring area; performing identification based on the multi-dimensional gas sensor network of the monitoring area to obtain the sensor response mode data; performing multi-sensor data fusion processing on the gas sensor array data of the monitoring area to obtain a gas concentration prediction model; performing gas concentration prediction on the monitoring area based on the gas concentration prediction model to obtain the original gas concentration prediction data of the monitoring area, and performing dynamic environmental adaptive correction on the original gas concentration prediction data of the monitoring area to obtain the gas concentration prediction data of the monitoring area. The present invention can improve the accuracy of gas concentration prediction.

A system for material detection and identification includes an interface configured to access a material database associating each of a plurality of materials with one or more corresponding resonance frequencies; an RF transmitter configured to, for each material of at least a subset of the plurality of materials in the material database, transmit into an environment an RF signal at a first resonance frequency for the material; an RF receiver configured to receive a response signal from the environment for each RF signal; and a processor configured to analyze each response signal for resonance characteristics that indicate a presence of the material and identifying the material to a user if the presence of the material is indicated by the resonance characteristics.

The present disclosure relates to systems, devices, and methods for automated cell sorting within a cell processing system. In an embodiment, the present disclosure relates to an automated cell sorting system comprising a cartridge having a cell sorting module, where the cell sorting module comprises a flow cell, and an instrument within a bay of a cell processing workcell, where the instrument comprises a magnetic array couplable to the flow cell, each of the magnets within the magnetic array having a width of w and being spaced apart by between about w / 3 to about ¾w, and wherein the flow cell has a height of between about 1 / 12w to about ⅛w.

The invention provides a gas concentration prediction method of a time convolution network based on a time attention mechanism, and belongs to the technical field of gas concentration prediction. According to the method, time series data are collected through multiple types of sensors, and after normalization and sliding window processing, a hybrid model integrating a time convolutional network, a gating circulation unit and a self-adaptive time attention mechanism is constructed. The time convolution network adopts an expansion causal convolution hierarchy to expand a receptive field, and captures a gas long-term accumulation effect; the gating circulation unit separates instantaneous fluctuation and trend characteristics through a double-time-scale architecture; the time attention mechanism dynamically distributes weights to strengthen key time steps. Through long-term and short-term feature fusion and dynamic optimization, the problem that a traditional model cannot give consideration to long-period dependence and short-term mutation is solved, the precision, stability and real-time performance of gas concentration prediction are remarkably improved, and the method is suitable for multi-component gas monitoring in a complex industrial environment.

The invention relates to an intelligent self-adaptive gas sensor adjustment controller and a working method thereof, and belongs to the technical field of gas detection. The adjustment controller comprises a main control module, a gas detection module, an environmental parameter module, an adjustment execution module, a communication module and a power management module, the main control module compensates detection parameters obtained by the gas detection module according to environmental parameter information obtained by the environmental parameter module in combination with built-in standard parameters and a compensation algorithm, and the adjustment execution module performs adjustment according to the compensation parameters. And whether the calibration execution module is started to calibrate the gas detection module is determined by judging the output stability of the gas detection module. According to the control method of the adjustment controller, through intelligent and self-adaptive adjustment control, the measurement precision, stability and applicability of the gas sensor are remarkably improved, and a reliable solution is provided for gas detection in a complex environment.

The invention belongs to the technical field of atmospheric pollution monitoring, and discloses a method for realizing VOCS inversion of satellite remote sensing column concentration by using machine learning. Comprising the following steps: preprocessing multi-source data to obtain a multi-source feature data set; performing feature hierarchical extraction and association analysis based on the multi-source feature data set to obtain a feature association graph; performing dynamic feature embedding and relation modeling based on the feature association graph to obtain a component feature space; based on the component feature space, constructing a multi-task deep learning inversion model, and performing weight optimization to obtain an optimized inversion model; based on the optimized inversion model, space-time interpolation and uncertainty analysis are carried out on satellite remote sensing column concentration data, and a high-resolution VOCs component inversion graph is obtained; and multi-scale verification and dynamic correction are carried out based on the high-resolution VOCs component inversion graph and the ground VOCs component monitoring data, and a VOCs component inversion result is finally obtained, so that the refinement level of regional atmosphere quality management is improved.

The invention discloses an integrated measurement method and system for hydrological elements in a high-salinity and high-sand environment, and relates to the technical field of hydrological monitoring, and the method comprises the steps: correcting the theoretical signal receiving strength of each sonar through an established sound attenuation model, and then calculating an effective signal-to-noise ratio, thereby achieving the measurement of the underwater topography in the high-salinity and high-sand environment. According to the method, a dynamic weight fusion algorithm is adopted to perform data fusion on the water depth data of each sonar to obtain fused water depth data, and an inversion method based on residual error minimization is adopted to perform iterative optimization on model parameters, so that the accuracy is relatively high. When the water velocity and the flow rate are measured in the high-salt and high-sand environment, iterative decoupling is carried out based on the sediment particle motion comprehensive model to obtain the sediment particle velocity, then the water velocity is updated based on the measured apparent velocity and the difference value, and after the updated water velocity is converged, the flow rate is calculated. And the accurate water flow rate can be calculated by combining the flow cross section area. The measurement precision of the unmanned ship on the hydrological elements in the saline-alkali area can be improved.

The invention relates to the technical field of road surface detection, in particular to a road surface skid resistance detection system based on multi-feature fusion, which comprises the following steps: applying broadband sweep frequency excitation by using a frequency modulation vibration sensor, matching with the inherent frequency of road surface texture to generate local resonance, and collecting the temperature, humidity and rainfall of a road surface in real time; performing fast Fourier transform on the collected vibration signals to obtain a resonance response spectrum, extracting key parameters through Gaussian fitting, and fusing frequency domain, material and environment data to form a comprehensive feature set; the frequency domain features are converted into three-dimensional energy distribution of pavement microtextures, the actual contact area ratio is calculated according to the three-dimensional energy distribution, an environment temperature and humidity compensation factor is introduced, and a dynamic friction attenuation coefficient is calculated; and comparing the calculated dynamic friction attenuation coefficient with a third-level safety threshold, and outputting a corresponding anti-skid performance level. Multi-source data fusion enables a detection result to be more fit with an actual driving scene, and misjudgment caused by single data is avoided.

The invention discloses an acid-resistant plate dark crack defect detection method and system, and relates to the technical field of acid-resistant plate defect detection.The detection method comprises the steps that multiple sets of multi-mode detection data in an acid-resistant plate detection scene are obtained, and the multiple sets of multi-mode detection data comprise polarized light appearance image data and micro-strain vibration data; and respectively extracting an appearance discriminant value, an ultrasonic discriminant value, a stress discriminant value and a micro-strain discriminant value from the modal data based on the improved twinborn attention network. According to the acid-resistant plate dark crack defect detection method and system, four-mode data of polarized light appearance, multi-frequency ultrasonic, flexible stress and micro-strain vibration are synchronously obtained through a multi-mode intelligent acquisition module: the polarized light appearance data captures surface shallow cracks, and the multi-frequency ultrasonic data penetrates through a plate body to identify deep cracks; the problem of missing detection of a traditional single mode is complementarily solved; in the analysis link, through a multi-modal attention and reinforcement learning fusion weight model, the weight of each discriminant value can be dynamically adjusted according to the environmental change, and the misjudgment of the fixed weight is avoided.

The invention belongs to the technical field of biological detection, and particularly relates to a monoclonal antibody 4F6 for HPV16 type E7 protein detection and application. The heavy chain variable region CDR sequences and the light chain variable region CDR sequences of the antibody are SEQ ID NO.1-3 and SEQ ID NO.4-6 respectively, and the antibody can be specifically combined with HPV16 type E7 protein without cross reaction. A double-antibody sandwich ELISA detection system constructed based on the antibody is combined with a biotin-avidin amplification technology, the sensitivity reaches 100 pg / mL, and the antibody is suitable for rapid detection of the HPV16 type E7 protein in a cervical exfoliated cell sample. The invention further provides the HPV16 type E7 recombinant protein which is obtained through prokaryotic expression and has immunocompetence, and the HPV16 type E7 recombinant protein is applied to antibody screening and detection. Compared with nucleic acid detection, the method has a lower false positive rate, can be used for early screening of cervical cancer and precancerous lesions, and has a good application prospect.

The invention discloses a garlic disease and insect pest dynamic diagnosis system based on temperature and humidity time sequence data, relates to the technical field of agricultural information processing, and solves the problems that in the prior art, a fixed parameter model is prone to sensitivity sudden drop, false alarm sudden increase and systematic deviation under sudden or semi-sudden changes of field environment and management variables. According to the scheme, semantic processing of temperature and humidity and farming events is carried out through an acquisition module, a system coupling module carries out segmented identification on distribution mutation caused by environment and operation, a domain representation module constructs causal threatening features, a prediction calibration module carries out dual-path drift decomposition and rapid correction, and a prediction result is obtained. The sample adding and label collecting module generates anti-fact samples and actively collects labels, and the decision attribution module outputs a structured evidence chain; according to the method, the dynamic adaptive capacity and reliability of the diagnosis system under the conditions of non-stationary distribution and concept drift are remarkably improved.

The present application provides a method and system for managing a BMS energy storage power supply based on gas concentration detection, including: receiving sampling data from a gas concentration sensor, establishing a gas concentration threshold judgment model, setting a leakage warning concentration threshold, and triggering a leakage warning signal when the concentration of the gas detected by the gas concentration sensor exceeds the leakage warning concentration threshold; if the leakage warning signal is received, increasing the sampling frequency of the gas concentration sensor within a preset range around the leakage point, and simultaneously starting the temperature sensor and the pressure sensor within the range to obtain multi-dimensional battery pack status data; performing a fusion operation on the multi-dimensional battery pack status data, and if the gas concentration continues to exceed the warning threshold and the temperature rise rate or the pressure rise rate exceeds the preset rate threshold, issuing a leakage confirmation signal and starting an emergency disposal mechanism.

The invention relates to pipeline circumferential weld detection, and discloses a pipeline circumferential weld defect detection method and system based on deep learning, multiple sensors are arranged on the surface layer of a pipeline to collect multi-modal signals, an inertial measurement device is combined to carry out pose correction, and a multi-modal time sequence data matrix is constructed; performing preprocessing and segmentation, calculating to obtain a dynamic reference band, and outputting upper and lower bounds; performing broken line fitting on each segment to extract a phase change factor triple, and comparing with a reference band to screen event candidate segments; constructing a multi-segment time domain coupling relation graph based on the candidate segments, and generating a dominant response mode candidate set; inputting a multi-branch time sequence coding network to extract a three-mode feature and generate a time sequence fusion feature; and performing similarity matching with a defect prototype library to obtain a defect category probability sequence, performing single-mode ablation and time sequence playback verification to obtain a high-confidence index, mapping the high-confidence index to a spatial position, and outputting a defect category and severity in combination with a feature index. According to the method, high-precision defect detection and reliable classification under the trenchless condition can be realized.

The invention provides a temperature and humidity compensation method and system for a gas sensor, and relates to the technical field of temperature and humidity compensation methods, and the method comprises the steps: obtaining the temperature and humidity of a space where the gas sensor is located and the time sequence data of the gas concentration output by the gas sensor at the same time, and obtaining a temperature input coefficient and a humidity input coefficient; establishing a temperature and humidity cross influence quantitative model to obtain a gas concentration error of the gas sensor caused by temperature and humidity changes; and filtering the gas concentration data output by the sensor before compensation, constructing a weighted comprehensive index of temperature and humidity changes, and compensating the filtered gas concentration data by adopting a dynamic switching compensation mode. And establishing a temperature and humidity cross influence quantitative model, inputting the temperature input coefficient and the humidity input coefficient into the established temperature and humidity cross influence quantitative model to obtain a gas concentration error, and adopting a dynamic switching compensation mode based on the gas concentration error according to the change of the environment temperature and humidity.

The invention discloses carbon emission monitoring equipment for constructional engineering, which comprises a carbon emission monitor, an angle adjusting assembly, a carbon emission monitoring device, a carbon emission monitoring device, a carbon emission monitoring device, a carbon emission monitoring device, a carbon emission monitoring device, a carbon emission monitoring device, a carbon emission monitoring device, a carbon emission monitoring device and a carbon emission monitoring device, and is characterized in that the carbon emission monitoring device is arranged below the carbon emission monitoring device; the air inlet filtering assembly comprises a mounting shell fixedly connected with the fixing strip; and an air injection assembly. The device has the advantages that through the mounted dust filter screen, the fixed frame, the roller brushes and other devices, the first lead screw motor drives the first sliding block and the fixed rod to move so as to drive the two sets of roller brushes to move, and the fixed rod moves to drive the gear to be engaged with the rack, so that the roller brushes rotate to clean dust and impurities on the surface of the dust filter screen; when the dust filter screen moves to the middle of the fixing frame, the first electric telescopic rod drives the fixing frame to extrude the spring, so that the sliding block is driven to impact the interior of the sliding groove, the fixing frame continuously vibrates, impurities at small positions on the dust filter screen can be promoted to fall off, and the cleaning effect is improved.

A method of delivering a receptacle to an instrument includes the steps of supporting a receptacle in a carriage, activating an electric motor to move the carriage between a first location and a second location of the instrument while the receptacle is supported by the carriage, applying a clamping force to the receptacle as the carriage moves from the first location to the second location, and releasing the clamping force from the receptacle as the carriage moves from the second location to the first location.

The system and method for processing a digital pathology image using a machine learning model that includes a self-supervised hierarchical Vision Transformer (ViT) configured to perform unsupervised clustering with multiple classification tokens. The method includes receiving a digital pathology image that depicts a tissue slice stained with histological dyes. The digital pathology image may be processed to generate a result comprising multiple predicted classifications of individual patches of the digital pathology image. The result is generated by a machine-learning model using a self-supervised hierarchical Vision Transformer (ViT) that may further comprise a multi-head self-attention module configured to predict a crosspatch relevance metric using an attention mechanism for each individual patch in the digital pathology image thereby assigning the individual patches to a cluster based on the crosspatch relevance metrics.