46810results about "Material analysis" patented technology

The invention relates to the technical field of the Internet of Things, and discloses a gas leakage monitoring method, device and equipment based on the Internet of Things and a storage medium, and the method comprises the steps: carrying out the data collection of a gas storage device, and obtaining an initial multi-dimensional data set; performing filtering and edge detection processing to obtain potential abnormal point data; inputting the potential abnormal point data into a mixed deep learning model for gas leakage detection to obtain initial leakage source position data and a leakage intensity value; executing multi-sensor cooperative accurate leakage positioning to obtain target leakage source position data; performing gas diffusion simulation to obtain gas concentration distribution data and risk indexes; based on the gas concentration distribution data and the risk index, dynamic reconstruction of the sensor network is carried out, an emergency response strategy and sensor network configuration information are output, and the method realizes whole-process management from leakage detection and source positioning to risk assessment and emergency response.

The present disclosure relates to systems, devices, and methods for automated reagent storage within a cell processing system. In an embodiment, the present disclosure relates to a reagent vault system comprising a refrigeration unit for storing cell processing reagents, a rotating carousel configured to receive one or more fluid devices, the fluid devices configured to store the cell processing reagents, and at least one sensor for measuring at least one parameter of the reagent vault system.

The present disclosure relates to a multi-factor coupling cooperative early warning method and system for fatigue crack propagation of a steel structure. The multi-factor coupling cooperative early warning method includes: obtaining multi-physical field monitoring data of a dangerous source distribution point of a steel structure project, and obtaining a monitoring time series data set; establishing an intuitionistic fuzzy matrix of the monitoring time series data set; obtaining uncertainties of indexes by using a grey relation coefficient between the monitoring indexes of physical fields; taking obtained uncertainties as basic probability assignments of evidences; preprocessing the evidences through weighed averaging, and obtaining corrected basic probability assignments; obtaining basic probability assignments of fatigue crack propagation of the steel structure at different development stages; and determining a fatigue crack propagation grade of the dangerous source distribution point of the steel structure project through the basis probability assignment.

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 discloses a condensation monitoring method for a digital substation. The method comprises the following steps: acquiring data information of a target digital substation; a high-risk area where condensation occurs is determined, and monitoring equipment is arranged to obtain monitoring data; selecting a monitoring frequency band, acquiring and analyzing data of the monitoring frequency band, and constructing a feature set related to the condensation degree; acquiring monitoring data of the target digital substation in real time and selecting an electromagnetic wave monitoring frequency band; feature extraction and analysis are carried out to construct a condensation monitoring model, and optimization is carried out; generating a condensation risk prediction result of the target digital substation based on the optimized condensation monitoring model; and giving an optimal anti-condensation measure for the target digital substation according to a prediction result, and completing condensation monitoring of the target digital substation. According to the method, the environmental parameters of the digital substation are acquired and processed, and the condensation prediction model is established, so that the condensation monitoring of the digital substation is realized, the reliability is higher, and the accuracy is better.

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.

Disclosed herein are methods, compositions, and kits for determining the presence or absence of a target polypeptide (e.g., a polypeptide involved in nitrogen fixation) in a sample (e.g., a sample of soil from the rhizosphere of a non-leguminous plant).

The invention relates to an aviation turbine blade service damage monitoring method based on digital twinning, belongs to the technical field of aero-engines, and solves the problems that an existing detection algorithm is high in calculation complexity and low in efficiency and precision. Determining a to-be-monitored turbine blade physical entity, and collecting turbine blade environment information; a digital twin is constructed on the basis of a physical entity, the turbine blade three-dimensional model construction module constructs a turbine blade three-dimensional model according to the physical entity, and the turbine blade operation environment simulation module performs environment simulation on the model according to real-time load, rotation speed and environment temperature data of a turbine blade. And the turbine blade defect evolution prediction module identifies blade surface defects of the model at each moment by using an intelligent defect detection algorithm to obtain the category, position information and size information of the defects, and performs evolution simulation and danger coefficient analysis. According to the invention, real-time on-line, high-precision and high-efficiency detection of the surface defects of the aviation turbine blade is realized, and the service life of the turbine blade is prolonged.

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 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 3 / 4w, and wherein the flow cell has a height of between about 1 / 12w to about 1 / 8w.

A system for analyzing measurements performed on a biological subject, the system including one or more processing devices configured to acquire subject data at least in part captured by a measurement system including at least one substrate including a plurality of microstructures configured to breach a functional barrier of the subject and a sensor operatively connected to at least one microstructure, the at least one sensor being configured to measure response signals from the at least one microstructure, and analyze the subject data using at least one model to determine an indicator at least partially indicative of a physiological state of the subject.

This invention discloses a method for comprehensively apportioning the source contribution to fine particular matter (PM2.5) based on the receptor model and the chemical transport model, which comprises the receptor model calculation steps, chemical transport model calculation steps and comprehensive source apportionment steps; the said comprehensive source apportionment step comprises the following sub-steps: according to the principle of inverse proportionality between uncertainty and weight coefficient, the first (receptor model) uncertainty and the second (chemical transport model) uncertainty are normalized to obtain their respective weight coefficients; the comprehensive source apportionment results are calculated based on the apportionment results of the receptor model and the chemical transport model, and their respective weight coefficient. The invention integrates the respective advantages of the receptor model and chemical transport model by using weight coefficients obtained through the normalization of uncertainties, thus improving the accuracy and reliability of the source apportionment result of PM2.5.

The invention discloses an intelligent working place dust monitoring and early warning system. The system comprises a dust sensor module, a data processing and storage module, a wireless communication module, an alarm module and a visual interface. The dust sensor detects the dust concentration in the air in real time, dust with different particle sizes is included, the data processing module carries out concentration calculation, trend analysis and acceleration monitoring, and dynamic threshold adjustment and trend prediction are supported. The system has a multi-stage alarm function, and when the dust concentration reaches a preset threshold value, early warning signals with different intensities are triggered to remind a user to take protective measures in time. The system not only improves the real-time performance and accuracy of dust monitoring, but also has automatic learning and intelligent early warning functions, is suitable for various high-dust environments such as mines, construction sites, workshops and the like, and remarkably improves the safety management level of workplaces.

An ecological management method and system for tropical marine ranching, a device, and a medium are provided, which relates to fishery resource management technologies. The method includes: obtaining ecological environment data and energy flow analysis data of a target marine ranching; performing ecological evaluation on the ecological environment data to determine an ecological comprehensive evaluation result, the ecological comprehensive evaluation result includes a health condition evaluation and an ecosystem stability evaluation of the tropical marine ranching; performing, based on the energy flow analysis data and the ecological comprehensive evaluation result, bio-capacity evaluation by using an Ecopath energy flow model to obtain a current bio-capacity; and performing ecological management and developing an ecological management strategy based on the current bio-capacity, the ecological management strategy includes an enhancement and releasing scheme and a fishing scheme. The method can obtain a unified evaluation standard and facilitate ecological management of marine ranching.

The invention discloses an air pollutant concentration prediction method and related equipment thereof, and relates to the technical field of data processing. The method comprises the following steps: acquiring current pollutant concentration data, current meteorological data and target site geographic position data corresponding to a target environment monitoring site; taking the current pollutant concentration data, the current meteorological data and the geographic position data of the target station as target input data of the prediction model; wherein an input module of the prediction model is used for performing modal decomposition processing on input data to obtain multiple pieces of modal data; the time feature extraction module is used for extracting time feature data; the spatial feature extraction module is used for extracting spatial feature data; the space-time fusion convolutional network module is used for extracting space-time features; the output module is used for outputting a prediction result; determining a prediction result of the air pollutant concentration corresponding to the target environment monitoring station according to the output result; the method achieves the effects of improving the comprehensiveness and accuracy of a prediction result and expanding application scenes.

The present invention relates to polypeptides which are covalently bound to molecular scaffolds such that two or more peptide loops are subtended between attachment points to the scaffold. In particular, the invention describes peptides which are high affinity binders of Nectin-4. The invention also includes drug conjugates comprising said peptides, conjugated to one or more effector and / or functional groups, to pharmaceutical compositions comprising said peptide ligands and drug conjugates and to the use of said peptide ligands and drug conjugates in preventing, suppressing or treating a disease or disorder mediated by Nectin-4.

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.

Systems and methods presented herein generally relate to greenhouse gas emission management and, more particularly, to a greenhouse gas emission management workflow to perform greenhouse gas detection sensor placement or greenhouse gas leak detection. For example, the system and method enable improved gas sensor arrangements within an oil and gas worksite. In another example, the system and method enable the prediction of a location and a leak rate of a gas leak within an oil and gas production facility based on measurements collected by gas leak sensors disposed within the facility and prevailing wind information.

The invention relates to a nano antibody targeting cadherin 17 and application thereof, and relates to the technical field of immunology and molecular biology, the amino acid sequence of a complementary determining region of the nano antibody targeting cadherin 17 comprises CDR1 shown as any one of SEQ ID NO: 21-32, CDR2 shown as any one of SEQ ID NO: 34-45, and CDR3 shown as any one of SEQ ID NO: 47-58. According to the invention, an alpaca immune phage display library is constructed, a series of nano antibody molecules targeting cadherin 17 (CDH17) are screened from the alpaca immune phage display library, and functions and applications of the nano antibody are verified at various levels of CDH17 antigen protein, CDH17 target cells, CDH17 CAR-T cells and the like.

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.

Described herein is a platform and supported graphical user interface (GUI) decision-making tools for use by medical practitioners and / or their patients, e.g., to aide in the process of making decisions about a course of cancer treatment and / or to track treatment and / or the progress of a disease.

Described herein is a platform and supported graphical user interface (GUI) decision-making tools for use by medical practitioners and / or their patients, e.g., to aide in the process of making decisions about a course of cancer treatment and / or to track treatment and / or the progress of a disease.

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 present disclosure relates to systems, devices, and methods for automated reagent storage within a cell processing system. In an embodiment, the present disclosure relates to a reagent vault system comprising a refrigeration unit for storing cell processing reagents, a rotating carousel configured to receive one or more fluid devices, the fluid devices configured to store the cell processing reagents, and at least one sensor for measuring at least one parameter of the reagent vault system.