87 results about "Sample Type" patented technology

The invention discloses an electric arc detection method based on differential increase and structured attention, and the method comprises the steps: firstly carrying out the collection and preprocessing of a current signal, carrying out the differential enhancement according to a sample type, carrying out the strong enhancement of an electric arc sample, improving the generalization capability, and carrying out the weak enhancement of a normal sample, thereby avoiding the overfitting; the problem of class imbalance is relieved, and the model generalization ability is improved; secondly, obtaining six complementary feature representations of time domain waveform, frequency domain frequency spectrum, time frequency analysis, envelope features, statistical distribution and related features from the differentially enhanced current signal through a multi-modal feature extraction method, and fusing to generate a multi-modal image; then, designing a deep learning model integrated with structured attention, carrying out distinguished attention on different feature analysis areas of the multi-modal image, and directionally enhancing arc features; and finally, dynamically quantifying the trained model, reducing the size of the model and reasoning delay, and supporting efficient deployment of various edge computing devices.

The invention discloses a gene sequencing sample data matching method based on a micro-fluidic chip, relates to the technical field of gene sequencing sample matching, and aims to solve the problem that the matching rate is reduced due to inaccurate analysis of gene sequencing samples. According to the method, multi-strategy comparison and candidate set screening are adopted, multi-dimensional comparison of sequences, variation and functions is combined, the accuracy is improved, the adaptability to complex samples is enhanced through quantitative index and biological verification evaluation and dynamic optimization of comparison strategies, the matching reliability and practical value are remarkably improved, the clinical and scientific research diversified requirements are met, and the method is worthy of popularization and application. Sample types such as blood and cells are adapted through a differential lysis strategy, impurities such as proteins and salts are removed through stepped purification, and the nucleic acid concentration and the fragment state are unified in combination with standardized treatment, so that interference is reduced from the source.

The invention relates to a chromatographic instrument temperature control method and system, an intelligent terminal and a storage medium, and relates to the technical field of chromatographic instrument sample separation.The method comprises the steps that the sample type of a preset sample is obtained; analyzing the sample type to determine a window heating rate and a window target temperature; acquiring a chromatographic column initial temperature of a preset chromatographic column; analyzing the initial temperature of the chromatographic column and the target temperature of the window to determine the temperature of the chromatographic column needing to be increased; analyzing the temperature required to be increased and the window heating rate of the chromatographic column so as to determine the heating time of the chromatographic column; and controlling a preset chromatographic column temperature regulation and control device to perform temperature control on the chromatographic column according to the chromatographic column heating time and the window target temperature. The method has the effect of improving the precision of the temperature retention time.

The invention discloses a microbe grouping analysis method and related equipment. The method comprises the following steps: acquiring microbe data of a target research area; based on microorganism abundance data, a conversion value of each sample is obtained through center logarithm ratio conversion processing, and then a distance value between any samples is obtained through Euclidean distance quantization; adaptively setting a minimum sample number and a neighborhood radius based on a sample number and a species number corresponding to the microbial data; performing density clustering on samples in the microbial data to obtain a clustering result, and further marking the sample type of each sample in combination with the minimum sample number and the neighborhood radius; performing multi-level analysis on the clustering result based on the sample type to obtain an analysis result of the clustering result; and performing correlation analysis based on the analysis result and the methane leakage data to obtain a microbe grouping analysis result of the target research area. The method realizes standardization and automation of the analysis process, can remarkably improve the analysis efficiency, and can be widely applied to the technical field of data processing.

The invention discloses a multi-modal large model Deepfake detection method based on retrieval enhancement, and relates to the frontier crossing field of artificial intelligence security, computer vision and multi-modal information processing, and the method comprises the steps: 1, carrying out two-stage labeling in sequence, constructing an evidence obtaining knowledge database through the first-stage labeling, constructing an evidence obtaining thinking chain data set through the second-stage labeling, and carrying out the second-stage labeling; wherein the evidence obtaining thinking chain data set is used for generating thinking chain data including a cross validation type, an evidence guide correction type and an adversarial sample type according to the validity of initial judgment and retrieval evidences; step 2, for an image to be detected, retrieving text evidences of Top-k similar cases from the evidence obtaining knowledge database by using a dynamic evidence obtaining retriever; and step 3, inputting an image to be detected and the retrieved text evidence into the critical reasoning multi-modal large model, performing multi-modal reasoning analysis, and outputting a detection result containing a reasoning process.

The invention relates to the technical field of ocean sample management, and discloses an ocean sample full-life-cycle digital management method, which comprises the following steps of: firstly, collecting characteristic data such as sample types, source positions and chemical component information of ocean samples, and calculating a classification scheme and storage parameters according to the characteristic data; evaluating a management demand and generating a target management strategy by combining the real-time management data of the sample library; making and optimizing a preliminary management plan by using algorithms such as k-means clustering and a decision tree in combination with historical management data and sample use frequency data, and generating a personalized management scheme; and finally, implementing the scheme, monitoring a sample preservation rate index through a sensor, dynamically adjusting a temperature threshold value and a storage position of classified storage equipment, introducing a real-time feedback mechanism to iteratively correct and adjust parameters, and generating a full-life-cycle management scheme. According to the method, full-process digital management of the ocean samples is realized, and the management efficiency and the sample storage quality are improved.

The invention discloses a metal material detection sample flow control method and system, and belongs to the technical field of metal material detection. The method comprises the following steps: acquiring detection sample type data, and preliminarily classifying detection samples according to the detection sample type data to obtain a preliminary classification result; performing size measurement on the preliminarily classified detection samples to obtain measurement data, and correcting the preliminary classification result based on the measurement data to obtain a corrected classification result; determining a testing machine distribution scheme of each detection sample according to the corrected classification result and the measurement data; based on the testing machine distribution scheme of each detection sample, starting a plurality of testing machines to detect the detection samples, and obtaining detection data of the plurality of testing machines; and dynamically adjusting a testing machine allocation scheme based on the detection data of the plurality of testing machines. According to the invention, the testing machines are reasonably distributed, and through dynamic flow control, high-efficiency and balanced operation of each testing machine when the number of samples is large is realized.

The application discloses a target detection model evaluation system and method based on light weight, relates to the technical field of target detection, and ensures data specification by acquiring a data set and converting the data set into a YOLO format, divides sample types by matching scores, and dynamically adjusts weights by using a segmented function to optimize classification; subsequently, an SPPF-Attention unit is constructed by combining a 1*1 point-by-point convolution, a depth-by-depth convolution and an MHSA module, and a light weight model is built; samples are expanded by Mosaic data enhancement, and the training process is controlled by using a segmented function; finally, the performance of the model is evaluated from multiple dimensions of parameter quantity, calculation quantity, mAP value and inference time consumption, the organic unification of model light weight, high-precision detection and high-efficiency inference is realized, the training efficiency and generalization capability are improved, and the multiple requirements of actual application are met.

The present application relates to the technical field of task scheduling, in particular to a sample pretreatment transfer process control system, which comprises a sample information management module, a sample state tracking module, a path scheduling module, a sample environment control module, a sample processing flow coordination module and a transfer process control module, the present application comprises a double-layer intelligent scheduling architecture composed of a strategy scheduling layer and a path execution layer, dynamically perceives task disturbance, and adjusts the scheduling sequence in real time through a hierarchical population collaborative guidance algorithm, thereby improving the task response efficiency and the flexibility of the overall operation of the system; the present application introduces an asymptotic path planning algorithm based on bidirectional potential induced search, and adopts a Bezier curve fitting in the path optimization stage, thereby realizing the collaborative optimization of path smoothing and dynamic obstacle avoidance; the present application introduces a temperature and humidity joint regulation algorithm based on fuzzy control in the sample environment control module, and perceives and adjusts the temperature and humidity and other environmental parameters in the transfer process in real time according to the sample type and detection requirements.

The invention relates to the technical field of production line management, and provides a full-automatic production line management method and system for phosphorus flame retardant pretreatment, and the method comprises the steps: building a resource demand model of a multi-medium pollution sample treatment task, and according to the type of a multi-medium pollution sample, a target treatment flow and preset technological parameters, obtaining a resource demand model of the multi-medium pollution sample treatment task; calculating an expected demand of each multi-medium pollution sample processing task on the shared key resource in a future preset duration; aggregating the expected demands of the multi-medium pollution sample processing tasks, and generating a whole production line future resource demand prediction map; obtaining shared resource availability data in real time, and comparing the shared resource availability data with a whole production line future resource demand prediction map and a preset resource supply period to identify a resource shortage risk; when a resource shortage risk is identified, making a resource allocation decision; and executing a resource allocation decision, and controlling the full-automatic production line. The full-automatic production line has the effect of improving the operation efficiency and stability of the full-automatic production line.

The application discloses a biomedical test strip detection data analysis method and system based on multiple algorithms. The method comprises the following steps: obtaining related data of a test paper to be detected, wherein the related data comprises T-line related data, B-line related data and C-line related data; verifying whether the lengths of three curves corresponding to the related data are consistent, if not, determining that the detection result is invalid, if yes, analyzing sample adding characteristics to determine whether there is sample adding sequence error or no sample adding; distinguishing sample types according to chromatography speed according to the related data; when the sample type is a whole blood type, calculating the length of a sample type identification point corresponding to three lines, adjusting and cutting off, and if the total length exceeds a set threshold, determining that a hemolysis phenomenon leads to an invalid detection result. The method of the application particularly focuses on processing the hemolysis phenomenon and other user operation related problems, thereby enhancing the reliability and efficiency of the overall detection system.

The invention discloses a sample feeding positioning system and a positioning method, and belongs to the technical field of material mechanics test equipment. The positioning system comprises an information acquisition unit, a sample positioning frame, a pressure sensor, a scanning assembly and a control unit, and the control unit receives initial information of a to-be-fed sample and generates a sample feeding number of the sample; receiving pressure data of the pressure sensor, comparing the pressure data with a gravity range of a sample specification corresponding to a preset sample loading position, and if the pressure data is greater than the gravity range, judging that the sample is placed wrongly and sending out a warning signal; the sample type identified by the scanning assembly is received, and if the sample type is inconsistent with the sample type corresponding to the sample loading position, a warning signal is sent out and the mechanical arm is locked; and if no sample is placed wrongly, the control unit sends a sample loading number and a grabbing instruction of the sample to be grabbed to the mechanical arm, and the mechanical arm grabs the corresponding sample. According to the sample positioning frame, multiple samples can be accurately positioned in the sample positioning frame, and the accuracy and the high efficiency of a mechanical test sample feeding link are improved.

The invention discloses an entry sample gamma radionuclide passive efficiency calibration method and system, and the method comprises the steps: collecting entry sample nuclide distribution, detector response, measurement geometry and other basic parameters, and then calling a ray direct penetration rate prediction model to calculate penetration rate data, an optimized detector parameter set is generated through a detector parameter characterization optimization model, correlation mapping of energy spectrum data and efficiency scale parameters is completed through an entry nuclide energy spectrum data analysis platform, target nuclides are recognized through a nuclide type recognition matching algorithm, feature parameters are extracted, and finally passive efficiency scales are completed. According to the method, a collaborative linkage mechanism is formed by integrating multiple models, a systematized data processing link is established, scenes with complicated types of entry samples and non-uniform nuclide distribution are effectively adapted, the accuracy and consistency of scales can be guaranteed without depending on a standard source, and efficient and reliable technical support is provided for entry nuclide detection.

This application provides a liquid analyzer and its automatic pipette tip selection detection method and storage medium. The method includes: acquiring consumable information and detection task information; the consumable information includes pipette tip model, available quantity of pipette tips, mixing cup specifications, and available quantity of mixing cups; the detection task information includes the detection items to be performed and the sample type; determining the sample volume and sample processing liquid volume that meet the pipetting conditions based on the detection task information and the detection method corresponding to the detection items; obtaining the pipetting coefficient of variation based on the sample type, sample volume, and sample processing liquid volume; selecting a target pipette tip model that meets preset detection conditions based on the pipetting coefficient of variation and the consumable information; and selecting the corresponding target pipette tip to perform the detection task and obtain the detection result. This method can reduce detection errors caused by improper pipette tip selection and effectively improve the accuracy and efficiency of detection.

This application relates to a method and system for identifying defects in corundum-coated ground. The method constructs a first feature distribution and a second feature distribution based on image features from X-ray image samples. The first feature distribution refers to a comprehensive feature data distribution formed by fusion of valuable and conventional sample features through a weighted summation method. This balances the influence of different sample types on the feature space, and the combination of valuable and conventional samples can represent important information about the data distribution characteristics, accurately reflecting the data distribution. The second feature distribution represents the feature distribution of pixels. Combining the comprehensive expression of the two distributions and matching them in a database allows for the matching of a more accurate sample set of data distributions. Finally, based on the X-ray image samples and the matched sample set, the defect detection model is trained, improving the accuracy of the defect detection model training.

The invention belongs to the technical field of bottom mud collection, and particularly relates to an artificial channel bottom mud separation and collection method which comprises the following steps: S1, controlling a sampling device to move to a to-be-detected area of an artificial channel; s2, collecting sediments at the bottom of the artificial navigation channel and putting the sediments into a detection chamber of a sampling device to detect the sample type; s3, if the detection sample is in a sandy type or the detection sample is abnormal and does not meet the collection standard, controlling the sampling device to find a new to-be-sampled area and repeating the step S2; if the detection sample is in a bottom mud type and meets the collection standard, performing the step S4; and S4, sampling the bottom mud in the to-be-sampled area, putting the sample into a sampling chamber of the sampling device, and controlling the sampling device to return after sampling is completed. According to the invention, through a separation and collection method of first detection and second sampling, a complex process of laboratory screening after blind collection in traditional sampling is avoided, generation of invalid samples is reduced from the source, the workload of subsequent laboratory analysis is reduced, and the sampling efficiency is improved.

Embodiments of the present application provide a data processing method and device, and a prediction model training method and device. The method comprises: determining feature information according to target data and access records of the target data; inputting the feature information into a prediction model to obtain time information of future access of the target data; the prediction model is trained by using training samples, the training samples comprising sample features, sample time labels and sample types, the sample time labels and the sample types being determined according to whether there are access records of data corresponding to the sample features before and after a random time within a sample sampling period; and identifying cold and hot data of the target data according to the time information. The sample time labels are used as labels of the training samples to train the prediction model, and then the time interval of next access of the target data based on the prediction model is used as a prediction result, and cold and hot data of the target data is identified according to the time interval, so that the accuracy of cold and hot data identification of the target data can be effectively improved.

The invention relates to the technical field of artificial intelligence, in particular to a multi-agent cooperative detection method, system and equipment based on a knowledge graph, and the method comprises the steps: receiving detection task data through a preset detection sample management agent, and carrying out the semantic understanding and structural processing of the detection task data through a preset large language model, and based on the processing result, performing knowledge routing in a preset knowledge graph to identify a sample type and determine a detection process, performing a detection experiment through a preset detection sample experiment recording agent according to the detection process, and interacting with a database of the detection equipment through a multi-modal data acquisition technology to acquire and record experiment data; and calling the knowledge graph through a preset report generation agent to perform automatic comparison on the experimental data, and performing intelligent judgment under the assistance of the large language model so as to generate a detection report. In this way, the automation level, efficiency and accuracy of detection can be remarkably improved.

The utility model relates to the technical field of vehicle inspection station sample detection, in particular to an equipment sample recognition and conveying mechanism for a vehicle inspection station, and the equipment sample recognition and conveying mechanism comprises a conveyor belt main body which is provided with a conveyor belt and a driving motor, the conveyor belt is in transmission connection with the driving motor, and the driving motor drives the conveyor belt to move when working; the sample identification camera is arranged on the adjacent side of the conveying belt and is used for identifying a sample image on the conveying belt and converting the sample image into a sample type signal to be output; and the sorting mechanical arm is arranged adjacent to the sample identification camera and is used for receiving the sample type signal sent by the sample identification camera and sorting the samples. The device has the effect of improving the working efficiency of sample detection of equipment.

The invention relates to the technical field of fire coal sample storage, in particular to a fire coal reference sample storage management system applied to a carbon emission control enterprise. The storage device comprises a controller, a sample storage chamber, a transfer assembly and an interaction assembly, the sample storage tank comprises a tank body, and the tank body is used for placing quantitative fire coal samples for future reference; when the tank body is picked up by the transfer assembly and placed on the positioning groove, the microprocessor in the tank body is electrically connected with the controller; and the controller reads the tank body identification code in the tank body, records the storage time, and stores the sample type, the tank body identification code, the storage time and the slot position information in a one-to-one correspondence manner to form a management database. According to the fire coal reference sample storage management system applied to the carbon emission control enterprise, a stable and good storage environment can be provided for fire coal reference samples of the carbon emission control enterprise, unified and effective management is realized, authenticity and traceability of samples submitted by a third party for inspection are ensured, and an effective evidence of the reference samples is provided for carbon emission accounting.

The present invention discloses a sample rack scheduling method, scheduling system, and analysis system. The method includes: obtaining the sample rack injection position of the sample rack that is triggered to inject and the sample type of the sample rack that triggers the injection and generating trigger information; determining the scheduling priority of the sample rack based on the trigger information, and controlling the scheduling distribution device to move to the corresponding sample rack injection position based on the injection priority of the sample rack; transferring the sample rack from the sample rack injection position to the scheduling distribution device; and transferring the sample rack to the target position via the scheduling distribution device. The sample rack scheduling judgment mechanism provided by the present invention can enable the scheduling distribution device to schedule and distribute sample racks to the maximum extent, save the residence time of the sample rack in the system, improve the operating efficiency of the sample processing system, and meet the demand for the shortest possible sample rack turnover time in clinical applications.

This utility model discloses a sample label sorting device for a laboratory, including a storage box; it further includes: a sliding groove on one side of the bottom of the storage box, a positioning tooth fixedly connected to the bottom surface of the storage box near the sliding groove, a sliding rod slidably connected to the middle of the sliding groove, a return spring sleeved on the outer side of the sliding rod, a sliding sleeve sleeved on the outer side of the return spring, and a locking tooth fixedly connected to the top of the sliding sleeve; wherein, a separator is fixedly connected to the top of the sliding rod, a limit rod is provided on one side of the top of the separator, a clamping spring sleeved on the outer side of the limit rod, and a clamping plate is attached to one end of the clamping spring. The separator can be moved by pulling, which facilitates the classification and storage of labels according to sample type or test item, and the position of the clamping plate can be adjusted by sliding, which facilitates the clamping of sample labels and thus prevents label confusion.

This disclosure describes a method for determining, in real time and continuously, an amount of particles of a given material in a sample, including steps of: E1: optically measuring a signature of the sample by an optical sensor; E2: identifying, by a processing unit, the type of the sample by a classification model trained on a training database including a plurality of reference signatures, each reference signature being associated with a reference sample type; and E3: determining, by the processing unit, the amount of particles of the given material in the sample based on the identified sample type and a correspondence table associating, with each of a plurality of reference sample types, a reference amount of particles of the given material.

The present application relates to the technical field of rail processing, and discloses an automatic processing method for rail samples, which comprises the following steps: obtaining image information and label information of a rail to be processed; obtaining a sample type to be processed, and calling a corresponding processing model from a preset processing model database in combination with the label information; automatically clamping and positioning the rail to be processed based on the processing model and the image information; sequentially performing bulk sample cutting, small sample decomposition and finishing on the clamped and positioned rail blank based on the processing model, so as to obtain a finished sample; and marking the finished sample and automatically discharging it. The method solves the problems of low processing efficiency, poor precision consistency, insufficient intelligence, and high safety risks caused by relying on manual operation in the processing of existing rail samples.

The invention discloses a remote sensing image labeling system and method. A basic support module of the labeling system provides visualization and editing functions of image and vector data and project management and system database management functions; the standard labeling process module provides a unified labeling process for various labeling types; the sample batch generation and conversion module provides various forms of sample batch generation and conversion functions; the labeling method comprises the following steps: firstly, creating a labeling project, setting a classification system and a standard labeling process mode, determining a standard labeling process according to the selection of the classification system and the selection of the labeling mode, and completing project construction and initializing the project by the system according to the selection; the system can carry out semi-automatic labeling, and sample batch generation and format conversion can be realized. According to the invention, sample data required by remote sensing image deep learning is efficiently manufactured in a standardized manner, all sample types of current remote sensing deep learning are covered, and the system and the method are a full-stack system and method.

A system, method, and computer program product for predicting a characteristic value of a sample based on a NIR / MIR spectrum obtained from the sample of a particular sample type are disclosed. A wavelet transform is applied to the obtained spectrum to compute a sample set of wavelet coefficients in a plurality of wavelet bands. The system accesses a library of a plurality of calibration sets of wavelet coefficients that define a calibration coefficient space. The calibration sets are computed by a wavelet transform of respective NIR / MIR spectra obtained from calibration samples of at least the particular sample type. Each calibration set is associated with one or more reference characteristic values of the respective calibration samples. The system creates a local PLS model of the characteristic value to be determined for the sample by selecting a predetermined number k of nearest neighbors of the sample set in the calibration coefficient space and computes the local PLS model based on the selected k nearest neighbors and their associated reference characteristic values. The characteristic value of the sample is predicted by applying the local PLS model to the sample set.

The invention discloses a sample data centralized optimization processing method applied to image recognition, and relates to the technical field of sample image data processing. According to the method, visual features of error sample data are extracted, clusters are obtained through a clustering algorithm, and cluster attributes are analyzed to determine gap sample types; calling an association model corresponding to the model task, matching a sample quality dimension corresponding to a model abnormal index, and screening out an abnormal target quality dimension through a pre-training model or manual verification of a sample data set; finally, a sample data set of the target model is optimized according to the logic that quality optimization is conducted firstly and then quantity optimization is conducted; the method breaks through the limitation that data requirements are judged according to experience in traditional static data set processing, on one hand, gap scenes which are not covered by a model are accurately positioned through clustering, and the problem that samples are redundant or key scene samples are insufficient is avoided; and on the other hand, low-quality data is removed in combination with the target quality dimension, and it is ensured that the supplemented samples have both the number standard and the quality adaptation.

The invention discloses a mutation back noise filtering algorithm based on next-generation sequencing data, relates to the technical field of high-throughput sequencing data analysis, and aims to solve the problems that the prior art depends on a baseline, and is poor in adaptability and high in false positive. The algorithm comprises the following steps: preprocessing sequencing data to obtain a pileup file, classifying sites according to a sequence context, calculating an error rate, constructing a layered back noise model to check mutation, qualitatively determining real mutation and back noise by combining a sample type, and evaluating sample quality. The method does not need to construct a base line in advance, adapt to hybrid capture and amplicon sequencing, can improve mutation detection specificity and repeated sample consistency, guarantees result reliability, and is suitable for scenes such as tumor gene detection.

The embodiment of the invention provides instant detection equipment. The instant detection equipment comprises a scanning assembly, an incubation assembly, a detection assembly and a processor, the scanning assembly is used for acquiring a detection item of a sample, the incubation assembly is used for incubating the sample, and the detection assembly is used for detecting the incubated sample to obtain a detection signal; the processor is used for controlling the incubation assembly to incubate the sample according to a detection item of the sample, and controlling the detection assembly to detect the sample to obtain a detection signal of the sample no matter whether the sample type of the sample is obtained or not after the incubation of the sample is finished; and obtaining a sample type of the sample set by the user, and determining a detection result of the sample according to the sample type and the detection signal of the sample. The instant detection equipment provided by the embodiment of the invention is relatively high in detection efficiency, and can prevent the situation that the sample incubation time is too long or the incubated sample is stored too long when the sample incubation is finished, so that the detection result of the sample is inaccurate.

The invention relates to the technical field of mineral detection, and discloses an intelligent acid addition accurate regulation and control method for customs mineral detection, which is used for solving the problem of insufficient digestion or excessive reaction caused by lack of intelligent regulation and control means for different mineral reaction characteristics in the acid addition process of the existing customs mineral detection. Sample matrix classification is achieved through spectral scanning before digestion, the digestion degree index and the acid activity cumulant are calculated in combination with temperature, heat flow, gas release rate, conductivity, turbidity and other multi-modal data in the digestion process, and the acid liquor adding mode and the heating rate are adjusted in real time based on model prediction control. According to the method, acid adding parameters can be adaptively adjusted according to sample types and reaction states, and incomplete digestion caused by insufficient acid adding and excessive reaction caused by excessive acid adding are avoided, so that the digestion effect consistency of different batches and different types of mineral samples in customs detection is ensured, and the accuracy and comparability of detection data are remarkably improved.