24 results about "Analysis sample" patented technology

A method for calculating total organic carbon (TOC) for a sample location includes obtaining an intact core soil sample from a sample location, the core soil sample having a depth and mass; transferring a predetermined fixed mass M from the intact core soil sample into an analysis sample container to obtain an analysis sample; and providing a subsample of the analysis sample for soil organic carbon (SOC) concentration testing.

The present invention addresses the problem of providing an analysis method with which it is possible to simply analyze an object to be analyzed in a solid sample. The analysis method according to the present invention performs any one of predetermined operations 1 to 4, and establishes a regression equation on the basis of the obtained signal intensity value and the amount of an object to be analyzed contained in a standard sample film removed by laser irradiation. And calculates the amount of the object to be analyzed in the solid analysis sample containing the object to be analyzed removed by the laser irradiation using a regression equation.

The invention relates to the technical field of label noise of table data, a distance measurement-based method and a neural network-based method are currently common label noise screening methods, and the methods are difficult to distinguish wrongly labeled samples and difficult samples with fuzzy categories near a decision boundary; according to the table data noise recognition and correction method based on Stein scoring, the difference of the attribution degree between sample and data feature distribution and label export distribution is analyzed, and the logarithmic probability density gradient of a sample is estimated through a diffusion model; moving the sample along a gradient represented by a scoring function by using a gradient method until the sample is converged to a centroid, forming a moving track moving from an original position of a sample space to a high-density centroid of data of a domain, and calculating the data feature distribution by comparing the differences of the directions and lengths of the moving track of the sample in the data feature distribution and the distribution exported by the label. Samples of potential tag errors in the tabular data are identified and tags thereof are corrected.

The application discloses a malware large model adaptive analysis system based on improved GRPO and dynamic and static cooperation, comprising a collection execution module, a static feature extraction module, a log processing module, a prompt word engineering module, an LLM analysis module, an evaluation feedback module, an analysis strategy optimization module and a weight optimization module, and also discloses a method.The beneficial effects of the application are that the static features of the sample file to be analyzed are intelligently fused with dynamic behavior logs, complete analysis context is provided for the LLM, comprehensive and high-precision threat determination of dynamic and static combination is realized, the optimal analysis strategy can be selected according to the log size, the large log is processed by using the context-aware block mechanism, and therefore, the analysis quality is guaranteed while the efficiency and cost are taken into account.

Embodiments of the present disclosure provide a method, an electronic device, and a computer program product for analyzing samples. The method includes acquiring a set of feature representations associated with a set of samples. The set of samples illustratively have classification information for indicating classifications of the set of samples. The method further includes adjusting the set of feature representations so that distances between feature representations of samples corresponding to the same classification are less than a first distance threshold. The method further includes training a classification model based on the adjusted set of feature representations and the classification information. The classification model is illustratively configured to receive an input sample and determine a classification of the input sample. In this manner, a relatively accurate classification model can be trained using a small number of samples, thereby reducing computation time and required computation capacity.

The invention relates to the technical field of waste heat utilization, in particular to a method for utilizing cooling circulating water of an analytical sampler at an outlet of a washing tower of a water-coal-slurry gasification furnace, which comprises the following steps: the circulating water enters the analytical sampler at the outlet of the washing tower of the gasification furnace; after the circulating water absorbs heat and is heated, the heated circulating water enters the temperature tracing pipeline through the cooling circulating water pipeline; the temperature tracing pipeline is connected to the analysis sampling pipeline in a winding manner, the heating circulating water provides heat for the analysis sampling pipeline through the pipe wall of the temperature tracing pipeline, and then the heating circulating water becomes cooling circulating water; the process gas in the gasification furnace enters an analysis sampler, and the cooled and extracted gas sample enters an analysis instrument for analysis and detection. The heating circulating water generated after the analysis sampler is cooled is directly used for heat tracing of the analysis sampling pipeline, waste heat absorbed by the circulating water is fully recycled, a heat tracing device does not need to be additionally arranged, the temperature of a gas sample in the sampling pipeline can be effectively maintained, and condensation of easily-condensed components in the sample due to temperature reduction is avoided.

The application relates to the technical field of marine meteorological data assimilation, in particular to a marine meteorological data assimilation method based on self-adaptive scale decomposition. An original data set is acquired and preprocessed to obtain a two-dimensional matrix. Based on an empirical orthogonal function analysis method EOF, dimension reduction is carried out on the initial matrix to obtain a spatial mode which does not change with time. A spatial self-adaptive scale reduction model is constructed by using the correlation of points on the spatial mode. Grid setting is carried out on the spatial self-adaptive scale reduction model to obtain an optimized multiple network. A target functional is determined and scale-by-scale assimilation is carried out to obtain an analysis field. An accuracy calculation formula and an accuracy threshold value are determined. Sample observation values and sample analysis values of a sample to be analyzed are acquired, and the accuracy is calculated. The two-dimensional matrix is extracted and processed by the EOF, the spatial self-adaptive scale reduction model is obtained, and scale-by-scale assimilation of adaptive matching of spatial characteristics is realized.

The embodiment of the invention provides a data analysis method and device and a storage medium, and belongs to the technical field of data processing and computers.The method comprises the steps that a target data set is obtained, and the target data set comprises text files of at least one format; carrying out asynchronous preprocessing on the target data set to obtain processed data, wherein the asynchronous preprocessing comprises data analysis and interpolation processing; various statistical analyses are conducted on the processed data to obtain analysis results, the analysis results comprise inspection result values and inspection types, and data feature analysis, sample size evaluation, distribution inspection and effect size calculation are conducted according to the inspection result values and the inspection types to obtain comprehensive evaluation data; determining an optimal statistical method combination according to the comprehensive evaluation data to determine an optimal analysis result; and setting an AI assistant for interpreting and describing the optimal analysis result to obtain an analysis report. According to the method, the data statistical analysis efficiency is improved.

Provided is an automated analysis system comprising an analyzing unit that analyzes a sample, and a control unit that controls the analyzing unit, wherein the control unit receives, via a network, a diagnosis measurement parameter that defines a condition for a diagnosis measurement, and controls the analyzing unit in accordance with the diagnosis measurement parameter to automatically perform the diagnosis measurement. The automated analysis system includes, for example, a quality control sample storage unit that stores quality control samples, and a conveying unit that conveys the quality control samples between the quality control sample storage unit and the analyzing unit, wherein: the diagnosis measurement parameter specifies, as the condition, the quality control sample to be used in the diagnosis measurement; and the control unit controls the quality control sample storage unit, the conveying unit, and the analyzing unit in accordance with the diagnosis measurement parameters, supplies the specified quality control sample to the analyzing unit, and performs the diagnosis measurement.

The invention provides a sample delivery system for an analytical instrument, comprising: a valve assembly comprising one or more valves, the valve assembly configured to receive an external sample from a sample source; an external sample reservoir coupled via the valve assembly to a sample dilution junction, wherein the valve assembly is configurable to alternatively permit either (i) flow of the external sample from the sample source to the external sample reservoir or (ii) flow of the external sample from the external sample reservoir to the sample dilution junction; a first fluid pump to control a flow of the external sample from the external sample reservoir to the sample dilution junction; a second fluid pump to control a flow of diluent to the sample dilution junction, thereby diluting the external sample to produce a diluted sample; and an analysis sample reservoir configured to deliver an analysis sample contained therein to an analysis device of the analytical instrument, wherein the analysis sample reservoir is coupled via the valve assembly to the sample dilution junction, and wherein the valve assembly is configurable to alternatively permit either (i) flow of the external sample along a continuous flow path from the sample source to the analysis sample reservoir, thereby by-passing the external sample reservoir, or (ii) flow of the diluted sample from the sample dilution junction to the analysis sample reservoir.

An object of the present invention is to provide an analysis method in which analysis of an analysis object in a solid sample can be simply performed. An analysis method according to the present invention includes: creating a regression equation based on a value of a signal intensity obtained by performing any one of predetermined operation 1, 2, 3, or 4 and an amount of an analysis object that is removed by laser light irradiation in a standard sample film; and using the regression equation to calculate an amount of the analysis object that is removed by laser light irradiation in a solid analysis sample including the analysis object.

1. The name of the design product: bottle (screw thread vial). 2. The use of the design product: for trace sample analysis sample bottle. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: combination state reference figure 1.

A data analysis method, system, and storage medium based on a bioanalyzer are disclosed. The samples to be analyzed include the test sample and standards. The method includes: acquiring the target channel signal measured by the bioanalyzer and determining whether the sample to be analyzed is a single-peak or non-single-peak sample based on the histogram of the signal; for non-single-peak samples, calculating the proportion of positive microspheres using the signal intensity corresponding to the deepest trough of the histogram as a first threshold; for single-peak samples, calculating the proportion of positive microspheres based on a second threshold, which is determined based on the first threshold of multiple non-single-peak samples of the standards; and calculating the average target capture amount of a single positive microsphere in the sample to be analyzed based on the interval where the proportion of positive microspheres is located and / or the signal intensity information of the positive microspheres. This method effectively extends the detection dynamic range and achieves automated and standardized data analysis. This application also discloses a corresponding system and computer storage medium.

The application provides a polysilicon production equipment fault diagnosis method and device, a server and a storage medium, and relates to the field of equipment fault diagnosis. The method comprises the following steps: acquiring target equipment collected sample data and first historical sample data within a preset time window; determining a first set composed of equipment parameters and a second set composed of equipment parameters and corresponding upstream and downstream process parameters; further determining the first robust distance of the equipment parameters according to the equipment mean vector and the equipment covariance matrix; determining the second robust distance of the equipment parameters and the corresponding upstream and downstream process parameters according to the overall mean vector and the overall covariance matrix; if the first robust distance is greater than the first robust distance threshold and / or the second robust distance is greater than the second robust distance threshold, the running state is determined to be an abnormal state. The comprehensiveness of the diagnosis and the accuracy of the diagnosis result are ensured, the stability of the benchmark during diagnosis is ensured, and the diagnosis efficiency is improved.

The application relates to the technical field of coal sample analysis, in particular to a method and device for determining a critical difference of 3mm storage and inspection coal sample inter-laboratory comparison, wherein the method comprises the following steps: taking a dry basis as a reference, respectively determining detection difference values of 0.2mm analysis samples prepared by a sending laboratory and a receiving laboratory, and calculating a 1.2T value according to detection items and index ranges, wherein T is a reproducibility critical difference specified in a corresponding detection standard; and comparing the detection difference values with the 1.2T value, wherein if the detection difference value is less than or equal to the 1.2T value, it is determined that the 3mm storage and inspection coal sample inter-laboratory comparison is qualified, otherwise, it is determined that the 3mm storage and inspection coal sample inter-laboratory comparison is unqualified. Therefore, the method solves the problems that related technologies use a square root of 3T as a critical difference of 3mm storage and inspection coal sample inter-laboratory comparison, the allowed range is widened, most of the comparison samples can pass the determination, and the sensitivity of the comparison result to detection abnormalities and quality fluctuations is reduced.

The present disclosure provides an improved workflow method for metabolic analysis of multiple samples in a sample set. The disclosed method utilizes a reference sample that represents the chemical complexity of the sample set. Analysis of the reference sample identifies relevant and irrelevant features, and based on these features, focuses the analysis on specific portions of the data obtained from each individual sample, thereby reducing the computational burden of analyzing all samples in the sample set. The present disclosure also provides a system for implementing the disclosed method.

The application discloses a label noise detection method based on a multi-time step loss sequence, utilizes a character recognition module to recognize characters from an input image; utilizes an MSL-MentorNet label noise detection module to analyze a loss sequence of the input image in a continuous training period according to a character recognition result, dynamically allocates sample weights to detect and filter noise labels, and feeds back screened data to a character recognition module to optimize model training. The application adopts an MSL-MentorNet label noise detection and noise filtering mechanism based on dynamic curriculum learning, analyzes a loss sequence of a sample in a continuous training period, utilizes a BiLSTM network to distinguish noise labels from clean difficult samples, and combines a linear scheduling strategy to gradually increase a training data amount. Based on the method of the application, the complex text image recognition accuracy can be improved, and the method is especially suitable for robust recognition and noise filtering of complex text images in scenarios such as ancient book digitization, historical document restoration, industrial OCR quality inspection and the like.

This utility model relates to the field of extraction mixture measurement technology, and discloses an online multi-channel measurement device for the concentration of extraction mixtures. The device includes a housing, with a control cabinet fixedly installed on the left side of the housing. This utility model directly acquires mixture samples from different extraction tanks via an input mechanism, and immediately inputs these samples into a detection mechanism for concentration measurement at a constant temperature. The samples are then discharged by an output mechanism into the corresponding extraction tank, achieving rapid online measurement of the concentration of multi-channel extraction mixtures. Analysis is performed in a detection device next to the production line, effectively managing the number of samples, reducing workload, eliminating sampling time blind spots, and ensuring more accurate test data because the detection environment is consistent with the on-site environment. Relevant data can be quickly fed back to the production department, allowing production personnel to adjust the liquid volume immediately based on accurate data with minimal time lag, significantly improving product yield.

The invention provides an analysis sample generation method based on multi-source heterogeneous data and a transient stability boundary, and the method comprises the steps: collecting the multi-source heterogeneous data, constructing a digital mirror image simulation model, and carrying out the feature extraction, and obtaining a time sequence feature and a waveform data space feature; after feature fusion, a digital mirror image simulation model is combined to obtain a digital twinborn model; dividing power angle data in the digital twin model so as to determine a transient stability boundary of the target power system; and performing time domain simulation sampling by taking the transient stability boundary as a constraint condition to generate an analysis sample of the target power system. According to the method, the multi-source heterogeneous data covering the real-time operation state, the fault, the load, the new energy output and the system parameters are collected, and it is ensured that the data dimension comprehensively fits the actual operation characteristic and state of the target electric power system; and the dynamic law of the system is precisely restored through a digital twin model, and sampling is carried out by taking a transient stability boundary as a constraint, so that the accuracy of sample analysis can be effectively improved.

[Problem]Provided is an analyzer that outputs information that makes it possible to trace a database and a log.[Solution]The analyzer includes: an analyzing unit that analyzes a sample; a storage unit that stores databases DB1 to DB3 in which a sample ID linked to personal information of a patient having supplied the sample, and sample information of the sample to be analyzed by the analyzing unit, an analysis result of the sample by the analyzing unit, or the personal information of the patient are stored in a corresponding manner and, a log including the sample ID; and a control unit that sets a unique identifier for each record of the database DB1, replaces the sample ID of the log with the identifier set for the sample ID of the database DB1 which is the same as the sample ID of the log (S1108), and after deleting the sample ID and personal information of the databases DB1 to DB3 and the log (S1112), outputs the databases DB1 to DB3 and the log to an external device (S1113).

The invention relates to a binary sample analysis method and device and electronic equipment. The method comprises the following steps: acquiring a plurality of binary samples, performing feature extraction on each binary sample to obtain image features, semantic features and behavior features, complementarily capturing complete semantic information of the binary samples in different dimensions, fusing the image features, the semantic features and the behavior features to obtain global features, and based on a large model, carrying out feature extraction on the global features. Carrying out representation learning on the global features to obtain feature representation of each sample, representing binary samples in a high-latitude space to enable the binary samples of the same family to be gathered in the high-latitude space, and subsequently analyzing the samples to be analyzed; the family to which the to-be-analyzed sample belongs can be evaluated by calculating the distance between the to-be-analyzed sample and each family prototype vector.

A novel online deep sample preparation device comprises a primary sampling and crushing unit, a subsample division and collection unit and a total sample division unit, the primary sampling and crushing unit comprises an automatic belt sampling machine and a 6mm crusher, and the automatic belt sampling machine is used for collecting raw coal subsamples; the subsample division and sample collection unit comprises a waste material division belt, a scraping and sweeping division device and a bottom opening sample collector, the scraping and sweeping division device performs preliminary division on a coal sample of a 6mm crusher, and redundant samples are removed by the waste material division belt; the total sample division unit comprises a total sample division belt and a double-sample sampler, the double-sample sampler adopts a double-sample belt middle sampler, one sample is guided into a full-water analysis sample bottle, and the other sample is guided into a general analysis sample bottle. The pneumatic conveying system is simple in structure and free of frequent action components, so that the reliability of unattended operation in the whole process is fundamentally guaranteed.

The invention provides a rapid argillization sample polished section and a preparation method thereof, and relates to the field of lithofacies sample preparation. The method comprises the following steps: dividing a collected argillization sample to obtain an analysis sample, and determining a sampling amount according to the fineness of the sample; mixing epoxy resin, a diluent and a curing agent to prepare a mixed liquid; filling part of the mixed liquid into the bottom of a mold, adding the analysis sample, stirring, and adding the residual mixed liquid until no obvious bubbles exist; heating at a first temperature, and stirring after heat preservation to obtain a first sample; heating and curing the first sample at a second temperature to obtain a second sample, and immediately cooling after heating and curing to obtain a sample block; and carrying out post-treatment on the completely solidified sample block to prepare the polished section. According to the preparation method, the high-quality argillization sample polished section can be provided for mining enterprises, the enterprises are helped to more accurately understand ore properties, the ore dressing process is optimized, and the resource utilization rate is increased.

A pretreatment cooling and dewatering device of an analysis system comprises an analysis sample inlet and an instrument air inlet, one side of the analysis sample inlet is connected with an analysis sample inlet pipeline, one end of the analysis sample inlet pipeline is connected to one side of a first-stage cooler, and the other end of the analysis sample inlet pipeline is connected with a second-stage cooler. One side of the primary cooler is connected to one end of a secondary analysis sample pipeline, the other end of the secondary analysis sample pipeline is connected to one side of the secondary cooler, and the other side of the secondary cooler is connected with an analysis sample outlet pipeline; according to the device, the cold air cooling capacity of the vortex refrigerator is fully utilized, the sample cold cutting efficiency is improved, vaporous water in an analysis sample is fully removed, stable operation of an analysis instrument is ensured, the operation failure rate of the analysis instrument is reduced, the labor intensity of maintenance personnel is reduced, and the production efficiency is improved.