124 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.

A method and apparatus for configuring a test project, a non-transitory computer-readable storage medium, a control terminal and a sample analysis system are provided. The method includes: acquiring a program mapping relation of a target test project, where the program mapping relation includes mapping relations between a plurality of target sample types and at least one target first experimental program; acquiring program configuration information of a target second experimental program, where the target second experimental program uniquely corresponds to a project type of the target test project; and configuring the target test project on the basis of the program mapping relation and the program configuration information of the target second experimental program.

The invention discloses a neural network model and system for QPCR (Quantitative Polymerase Chain Reaction) result analysis. A CT value, fluorescence signal values under all different cycle times in the PCR process, fluorescence signal amplification values under all different cycle times, and the change curvature of the fluorescence signal amplification values under all different cycle times in the PCR process are used for calculating the fluorescence signal values under all different cycle times; at least three types of QPCR result data in the slopes of fluorescence signal amplification value changes under all different cycle times are used as input, the sample type of the target gene corresponding to the QPCR result data is used as result output, and the method is used for predicting the sample type of the target gene of an unknown sample. According to the multi-dimensional QPCR result analysis system based on the neural network model, the multi-dimensional QPCR result data serves as an analysis object, the neural network model is combined, and the detection sensitivity and the classification precision are remarkably improved by capturing fine differences of QPCR results between samples.

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.

Embodiments of the present application provide a data processing method and a related apparatus. The method comprises: obtaining a plurality of pieces of sample data about a service system, wherein each piece of sample data comprises feature data, label data, and a confidence level; on the basis of a plurality of pieces of first sample data, generating second sample data of which the sample type is "black sample", wherein the first sample data comprises sample data having the confidence level higher than a preset threshold among the plurality of pieces of sample data; and generating a new training sample set on the basis of a historical data training sample set and the second sample data, wherein the training sample set is used for training a service model, and the service model is used for performing sample type prediction on the basis of service data in an operation process of the service system. By using the embodiments of the present application, the quality of the training sample set can be improved, thereby improving the performance index of the trained model.

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 utility model discloses an anti-infection sputum swab double-sample collecting device. The collection device comprises an anti-pollution sputum collector, a double-sample collection tube and a collection tube cover, the anti-pollution sputum collector comprises a collector tube body, and a silica gel ring is arranged at an end opening of the upper end of the collector tube body. The double-sample collecting tube is a double-cavity collecting tube and comprises a sputum cavity and a swab cavity which are separated by a partition plate; the collector tube body is connected with the sputum cavity in a clamping manner; the silica gel ring can be attached to the periphery of the mouth of the human body to form sealing. The double-sample collection device disclosed by the utility model is suitable for simultaneously collecting two sample types, namely sputum and swab, in hospitals, communities or personal sites, a plurality of parts are matched differently to realize simultaneous collection of the two samples, and repeated uncovering and one-time covering are not needed, so that aerosol pollution and personnel infection are avoided; in addition, the cotton fracture is designed at the inlet of the swab cavity, so that the infection risk caused by manual sample touching and sample splashing is reduced.

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.

The invention discloses a sample detection system and method. The system comprises a sample detection module and a report generation module, the sample detection module is used for determining the sample type of a to-be-detected sample; determining a detection item matched with the sample type according to the detection standard knowledge base; the detection standard knowledge base comprises a matching relationship between sample types and detection items; detecting the to-be-detected sample according to the detection item to obtain at least one item of detection data; and the report generation module is used for generating a sample detection report according to the detection data of the sample detection module. According to the technical scheme, the sample detection module and the report generation module are integrated, data intercommunication is achieved, the automatic detection process of the to-be-detected sample and the automatic generation process of the detection report of the to-be-detected sample are achieved, closed-loop management of the detection process is achieved, the data island problem is solved, and the detection efficiency is improved. The operation efficiency of a sample detection laboratory is improved.

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 utility model provides a combined medical specimen temporary storage device which comprises a plurality of specimen placing units which are sequentially overlapped in the height direction of the combined medical specimen temporary storage device, and each specimen placing unit is provided with a plurality of storage grooves used for placing sample test tubes, so that the combined medical specimen temporary storage device provides great flexibility for temporary storage and inspection of samples, for example, the sample test tubes can be placed in the storage grooves. Under the condition that more samples are sampled in a short time, two or more sample placing units can be used for placing samples of the same type; under the condition of more sample types, a plurality of sample placing units can be used for respectively placing different types of samples; according to the sample condition, all the sample placing units or a part of the sample placing units can be overlaid and then submitted for inspection, and due to the fact that each sample placing unit is provided with the corresponding embedding groove and the corresponding embedding block, every two adjacent sample placing units can be embedded through the embedding grooves and the embedding blocks of the sample placing units. Therefore, after being stacked, the plurality of specimen placing units are relatively stable as a whole, and the inspection efficiency can be improved.

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.

The invention provides a method and a system for detecting non-metallic inclusions in steel based on background reconstruction. The method comprises the following steps: acquiring a to-be-detected input image; inputting the input image to a background reconstruction module to generate a reconstructed image after inclusion removal; calculating a difference value between the input image and the reconstructed image to obtain a detection result image containing inclusions; the detection result image is processed through a post-processing module to output an inclusion detection result, and the method is low in data labeling requirement, is not influenced by unbalanced inclusion sample types, is higher in algorithm adaptability and robustness, and is higher in accuracy. The problem that a method for detecting non-metallic inclusions in steel is difficult to meet high-precision and high-efficiency quality inspection requirements is solved.

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 provides a face recognition method for missing label data of the user side, including selecting an initial model and determining a sample selection strategy. Supplementary samples are screened out from the unlabeled sample set based on the sample selection strategy to obtain a supplementary sample set. The supplementary sample set is added to the labeled sample set to obtain a first training set. The first training set includes labeled samples and unlabeled samples, and the initial model is trained hybridly using the first training set to obtain a face recognition model. In semi-supervised training, first, the labeled samples are used to learn the mapping relationship between the input features and the output labels, and then the sample types of the unlabeled samples are predicted. The unlabeled samples contain a large amount of unstructured or unannotated data. The present invention combines the labeled samples and the unlabeled samples, which can solve the problem of falling into a local optimal solution when using active learning alone, accelerate the convergence process of the initial model in the case of lack of label data, and thus improve the efficiency and accuracy of training the initial model.

The invention relates to the technical field of coating quality prediction, and discloses a battery pole piece coating quality prediction method, system and device and a storage medium, and the method comprises the steps: obtaining the coating time sequence data of a battery pole piece, calculating the similarity between the coating time sequence data and the clustering centroid corresponding to each preset sample type, and obtaining the coating quality of the battery pole piece according to the similarity. Obtaining a sample type of the coating time sequence data; selecting a time sequence prediction model corresponding to the sample type from a pre-established time sequence prediction model set, and inputting the coating time sequence data into the time sequence prediction model to obtain coating time sequence prediction data; and determining the coating quality of the battery pole piece according to the coating time sequence prediction data. According to the method, the high-quality sample data set is established, and the classified time sequence prediction model is adopted to predict the coating data, so that the accuracy of a coating quality data prediction result is effectively improved, and the quality and efficiency of battery pole piece coating production are improved.

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.