339 results about "Sample quality" patented technology

The invention relates to the technical field of power supplies, and discloses a high-precision power supply control method based on a digital signal processor, which comprises the following steps of: acquiring data such as voltage, current, temperature, load parameters and the like of a power supply at a high frequency of kHz-level sampling frequency by using a high-precision sensor, transmitting the data to a DSP (Digital Signal Processor), and removing noise interference by applying wavelet transform. Sample quality is improved through high-frequency data acquisition and wavelet denoising, nonlinear and time-varying characteristics of a power supply system are identified by using a lightweight CNN and an LSTM, a reinforcement learning dynamic optimization control strategy is combined, self-adaptive switching between traditional control and an intelligent algorithm is realized through working condition identification, and the power supply system control method based on the LSTM is realized. The problems that noise interference influences data quality, system characteristic recognition is inaccurate, complex working condition control strategy optimization is insufficient, and output precision is unstable due to the fact that a control mode cannot be switched in a self-adaptive mode in an existing power supply can be effectively solved, and control precision, efficiency and device loss can be considered. And the stable operation requirement of the high-precision power supply under multiple working conditions is met.

The invention belongs to the technical field of electrical equipment detection, and discloses a defect image enhancement method fusing reasoning and generation, which integrates visible light, infrared and laser radar data through a multi-modal feature fusion network, breaks through the limitation that a contrast file CN114281093A only depends on a visible light image, and improves the detection accuracy. The dynamic attention mechanism can flexibly deploy visual, spatial and semantic feature weights according to defect types, key features can still be captured in complex environments such as strong light and shielding, and meanwhile, the spatial form of the defects is analyzed by means of three-dimensional point cloud; by means of the design, missing detection caused by insufficient characteristics of tiny parts such as hardware fittings and pins is effectively avoided. Aiming at the problem of distortion of a sample generated by a traditional data enhancement method in a comparison file, the sample quality is guaranteed through double mechanisms of reasoning constraint and physical verification, defect features output by a reasoning model directly constrain feature distribution of the generated sample, and meanwhile, a material mechanics rule is introduced to verify the physical rationality of the generated sample.

The invention relates to the field of computer vision algorithms, in particular to a defect detection data screening method based on a Pontryagin maximum principle, which comprises the following steps of: training a training data set and testing an evaluation data set to obtain a model reference index; based on a preset proxy data set, respectively calculating definition, labeling integrity and distribution deviation degree indexes to obtain an initial quality weight vector; iteratively updating the basic model parameters and reversely iteratively updating the target vector to obtain a sample quality score; a training scoring device scores and sorts full samples of the training data set; and dividing the sorted full samples into a plurality of candidate screening intervals, and screening high-quality data to train a final model. According to the method, the entropy weight method is adopted to weight the three dimensions to obtain the initial quality weight, so that the initial weight of the sample can reflect the own basic quality difference, the problem that the traditional uniform weight ignores the sample quality difference is avoided, and the accuracy of the sample quality score is further improved.

The invention provides a sampling method suitable for continuous-discrete mixed variable design optimization of an advanced reactor. The sampling method comprises the following steps: (1) screening an effective discrete variable combination; (2) constructing a cumulative probability distribution function of the discrete variable combination; (3) hierarchically designing a space based on a cumulative probability distribution function interval; (4) distributing the number of samples according to the interval width proportion of the cumulative probability distribution function of each layer; (5) Latin hypercube sampling is carried out on the continuous variables in each layer; (6) integrating a global sample set and verifying the quality of the global sample set; and (7) constructing an agent model and evaluating precision. According to the method, the probability characteristic of the discrete variable and the space filling characteristic of the continuous variable are integrated, efficient exploration of the nuclear reactor design space is achieved, and compared with a traditional method, the sample quality and the calculation efficiency are remarkably improved.

The invention discloses a tea review expert decision support method and system based on a knowledge graph, and relates to the technical field of tea quality evaluation, and the method comprises the steps: collecting tea sample data and expert review data, calculating an expert score consistency index, and constructing an expert decision knowledge graph; analyzing an expert score distribution rule to obtain a rule mapping weight, mapping the standardized features to a knowledge graph and generating a feature transmission path; correcting a transmission result according to the rule adaptability and the conflict degree to obtain a tea sample quality score and a feature analysis chart; and finally, converting the feature analysis graph into a process control rule sequence, determining an execution priority based on the quality score, and generating a process parameter adjustment scheme. According to the invention, the intelligent optimization of the tea production process is realized, and the stability and consistency of the tea quality are improved.

The invention discloses a multi-generation sequencing data quality control management method and system, and relates to the technical field related to data management, and the method comprises the following steps: reading file header data of sequencing data, and loading a quality control rule template; after the sequencing data is imported and converted, feature extraction is carried out under single read granularity, and a feature extraction result is established; performing multi-layer nested quality control analysis according to the quality control rule template, the sequencing data and the feature extraction result, and establishing a sample quality grade identifier; configuring a trigger threshold value, performing trigger analysis on a sample quality grade identifier, executing traceability detection, and establishing an abnormal influence path diagram; and correcting the sample quality grade identifier and generating a quality control analysis result. The technical problems that in the prior art, due to the fact that the sequencing data quality control dimension is single, the dynamic adaptability is poor and the deep anomaly recognition capacity is insufficient, the sequencing data quality control management quality and reliability are poor are solved, and the technical effects that multi-dimensional accurate quality control is achieved, and the sequencing data quality control management quality and reliability are improved are achieved.

The invention discloses a data set construction method and device and a medium, and the method comprises the steps: carrying out the processing of original data through a standardized logic program and a big language model cue word, and obtaining a standardized processing text; extracting a field label of the standardized processing text through a large language model cue word, and constructing an initial training sample set according to the field label; performing quality evaluation on the initial training sample set through a verification set evaluation function to obtain a quality evaluation result; performing question extraction on the quality evaluation result through a large language model, and generating prompt word modification suggestions; and re-inputting the adjusted cue word into the large language model to generate a new training sample set, and carrying out loop iteration on the processes of quality evaluation, question extraction and cue word adjustment until the sample quality reaches a preset quality standard. Through combination of large language model cue words and an adaptive optimization algorithm, the data set construction efficiency is improved, and the pain points which depend on manpower and are difficult to adapt to large-scale complex data processing are solved.

The invention provides a steel plate surface defect image generation method based on a de-noising diffusion probability model and a visual Transform, relates to the technical field of industrial surface defect detection, and aims to solve the problems of low quality of generated samples, lack of detail richness and insufficient global dependency relationship modeling capability in the conventional defect image generation method. The performance improvement of a defect detection algorithm is limited; according to the generation method provided by the invention, by introducing the ViT model, the capability of capturing the global dependency relationship in the image by the model is remarkably enhanced, the limitation of the traditional DDPM in modeling the global structure of the image is overcome, the diversity and quality of the generated image are effectively improved, and the method is suitable for further improving the modeling precision of a complex defect structure. A channel-space self-attention module is designed, and the CSSAM of the module can dynamically adjust the attention between different space regions and channels, so that the details of complex defects are finely simulated, and the authenticity and high fidelity of a generated image on the aspects of a global structure and local textures are ensured.

The invention relates to a urine specific protein detection sample quality screening system and screening method. The screening method comprises the following steps: firstly, creating a urine specific protein quality model; then collecting an image of a to-be-detected sample; and finally, analyzing and evaluating the sample to be detected according to the sample quality model and the image of the sample to be detected, and converting urine colors (such as light yellow, deep yellow, hematuria and the like) into quantifiable numerical values by collecting the image of the sample (urine). Compared with traditional naked eye observation, judgment deviation caused by subjective factors such as experience and ambient light of detection personnel is avoided, and the detection result is more objective and repeatable; according to the method, the sample image is converted into quantifiable numerical values so as to carry out numerical analysis on the detection data of the color, clarity and turbidity of the sample, hematuria and urine with high turbidity in the sample are automatically screened out, manual operation steps are reduced, the detection efficiency is improved, and the method is particularly suitable for clinical or large-scale screening scenes.

The invention relates to the field of geotechnical engineering equipment, and discloses a geotechnical engineering investigation rock sample collecting device which comprises two bases, the top ends of the bases are fixedly installed on the two sides of the bottom end of a supporting plate correspondingly, and vertical rods are fixedly installed on the two sides of the top end of the supporting plate correspondingly; a threaded rod is movably mounted in the middle of the bottom end of the transverse frame, the bottom end of the threaded rod is movably mounted in the middle of the top end of the supporting plate, the outer diameter of the threaded rod is in threaded connection to the middle of the rear end of the box body, an outer cylinder is movably mounted at the inner bottom of the box body, a middle cylinder is movably mounted in the inner middle of the box body, and the bottom end of the middle cylinder penetrates through the outer cylinder and extends to the position below the outer cylinder; and an inner cylinder is mounted in the middle cylinder through a positioning bearing. Core completeness is guaranteed through fracture prevention and buffer clamping design, pollution is reduced through an efficient cooling chip removal system, reliable operation of equipment is achieved in combination with overload protection and a stable structure, and sampling efficiency and sample quality are remarkably improved.

The invention is suitable for the technical field of material sintering, and provides a floating type sintering platform demolding and sampling device which comprises a sintering platform, a floating lower electrode module, a floating mold module, a lifting mold frame, a demolding and mold cleaning module and a sample collecting and electrode cleaning module. The lifting mold frame is provided with a first platform and a second platform, and the first platform penetrates through the floating lower electrode module through a first limiting groove to jack the floating mold module to a set height; the sample collecting and electrode cleaning module slides to the position below the mold module through the second limiting groove, and sample receiving and electrode cleaning are achieved. High-temperature online demolding can be achieved, the sample breaking risk is reduced, and the breaking problem caused by the difference of thermal expansion coefficients is avoided; the device integrates demolding, mold cleaning, electrode cleaning and sample collecting functions, is compact in structure, reduces the occupied area and complexity of equipment, prolongs the service life of the mold and the electrode, and improves the sample quality.

The invention discloses a construction method and application of a specific chromatogram of a vinegar trogopterus dung water extract. The construction method of the specific chromatogram of the water extract of trogopterus dung processed with vinegar comprises the following steps: mixing the water extract of trogopterus dung processed with vinegar with an extraction solvent, and extracting to prepare a test solution; and performing high performance liquid chromatography analysis on the test solution, and constructing the characteristic spectrum of the vinegar trogopterus dung water extract. The construction method of the specific chromatogram of the vinegar trogopterus dung water extract is high in precision, has repeatability and stability, and can provide more abundant characteristic peak information, so that the sample quality can be reflected more comprehensively.

The invention discloses an ion thinning instrument control method based on a self-adaptive gradient energy reduction algorithm, and belongs to the technical field of transmission electron microscope (TEM) sample preparation. The problem that a traditional ion thinning technology has many limitations is solved, dynamic optimization of parameters such as ion beam energy, beam current density and sample temperature is achieved through the self-adaptive gradient energy reduction algorithm, and the sample preparation efficiency is remarkably improved; by combining multi-modal data acquisition and dynamic gradient optimization, the thinning process can be accurately controlled, and the sample quality is improved; sample damage is effectively avoided by combining a damage constraint mechanism and self-adaptive step length control, and the stability of the preparation process is improved; the method is suitable for various materials through a pre-stored optimization path library, and has wide applicability; therefore, the energy of the ion beam is optimized, the incident angle and the beam density are dynamically adjusted, and the thinning instrument is controlled.

The invention discloses a cell slice sampling device, which belongs to the technical field of biomedical detection equipment, and comprises a sampling table and a sample fixing mechanism arranged on the sampling table, the three-dimensional adjusting mechanism comprises an X / Y-axis translation assembly and a Z-axis feeding assembly, the X / Y-axis translation assembly drives a worm gear through a worm to achieve plane positioning, and the Z-axis feeding assembly controls vertical displacement through a second threaded rod penetrating through an inner thread of an assembling block; the cutting mechanism comprises a blade driven by two groups of belt pulleys and an assembling shell; the collecting mechanism comprises a rotatable turret and a plurality of collecting tanks. The submillimeter-level positioning precision of the three-dimensional adjusting mechanism (X / Y / Z axis) is set, precutting and the main blade work cooperatively, uniform slice thickness and high integrity are ensured, and through reasonable structural design, accurate and efficient sampling of tissue samples is achieved, manual operation errors are reduced, and the sample quality and the reliability of detection results are improved.

Diffusion models are machine learning algorithms implemented as neural network-based denoisers that are uniquely trained to generate high-quality data from an input lower-quality data. However, for complex datasets, the samples generated by a diffusion model can still fail to reproduce the quality and diversity of the training data, due to approximation errors made by the finite-capacity network. Diffusion guidance addresses this issue by steering the sampling process away from a less desired model, toward a preferred one. However, current guidance methods rely on a single additional denoiser and manual tuning of guidance weights, which is suboptimal for large, complex models and leads to inefficiencies. The present disclosure employs a mixture of denoisers to guide a diffusion model, which can increase the expressiveness of the guidance and substantially improve sample quality and diversity in the output of the diffusion model.

The utility model discloses a cage rearing endangered bird excrement collecting cage which comprises a cage body, a log roost is arranged in the cage body, a conveying assembly is arranged at the bottom of the cage body, the conveying assembly is arranged below the log roost, the conveying assembly comprises a pair of sliding rails parallel to the log roost, a sliding disc is movably installed on the sliding rails, and the log roost is arranged on the sliding disc. A fixing pin is arranged at the bottom of the sliding rail, an excrement collecting disc is placed on the sliding disc, a supporting plate is arranged below the excrement collecting disc in parallel, a plurality of rubber damping bodies are installed between the supporting plate and the excrement collecting disc, and the excrement collecting disc is fixedly covered with a sterile plastic film through a buckle. The cage body is provided with a rectangular frame opening corresponding to one ends of the pair of sliding rails, an iron sheet movable door is installed in the rectangular frame opening, and the cage body is provided with a net door and an inner net door which are arranged in parallel front and back and can only be opened and closed in one direction. And the bird stress risk is reduced.

The invention relates to a unified system for the extraction and purification of biomolecules using a novel Cloudy-Precipitated (CP) Buffer and an integrated extraction device. The CP Solution remains cloudy even when heated and enables single- or multi-step workflows for isolating nucleic acids without requiring multiple buffers or organic solvents. It contains optimized concentrations of detergents, chaotropic agents, salts, and adsorbents to efficiently lyse cells and minimize processing steps. The associated device incorporates a heating and filtration system that allows separation of target molecules from inhibitors or impurities based on molecular weight. It can operate as a single- or double-step configuration with various filter membrane options. This system reduces time, labor, and environmental impact while enhancing sample quality for molecular diagnostics, research, and therapeutic applications.

The presently described techniques utilize detection p-value metrics as part of determining sample quality in the context of a methylation assay. In these approaches, the detection p-value may be understood to be the probability of observing a signal at least as extreme as the measured or detected signal. Such a detection p-value is used to assess quality of individual probes. Based on the detection p-values for a given set of probes, the quality of a given sample may be assessed as a detection rate, which may be understood to be the proportion of probes passing a p-value detection threshold.

The invention discloses a gene sequencing data management method for whole genome methylation sequencing, and relates to the technical field of gene sequencing data management.The method comprises the steps that original gene sequencing data are obtained and preprocessed, and a regional methylation matrix is generated; calculating a technical difference measure and a phenotype difference measure of each region unit based on the region methylation matrix and the sample metadata, identifying a technical methylation bias domain, and grading the technical methylation bias domain into a steady-state technical methylation bias domain and an event-driven technical methylation bias domain according to the occurrence frequency of the technical methylation bias domain in multiple batches; based on a linear adjacency relation and a co-bias relation, constructing a technical methylation bias super-domain and classifying the technical methylation bias super-domain; performing regional shielding on the methylation bias super-domain of the steady state technology, performing normalization correction on the methylation bias super-domain of the event-driven technology, and generating a corrected regional methylation matrix; calculating a sample quality index based on the corrected matrix and performing quality grading; according to the method, technical bias can be identified and corrected, and the methylation data quality and analysis reliability are improved.

The invention provides an image data set construction method and system based on multistage collaboration and a model training method. The image data set construction method comprises the steps of obtaining an initial image pair set; respectively estimating the average depth of pixel points corresponding to foreground objects in the source image and the target image and the consistency of backgrounds; based on the average depth and background consistency result obtained through estimation, screening image pairs meeting a depth change condition and a background consistency condition from the initial image pair set to form a geometric-level image pair set; based on a large visual language model, screening out image pairs with perceptible depth differences of foreground objects from the geometric-level image pair set to form a semantic-level image pair set, and generating natural language texts corresponding to the screened image pairs; and determining a depth difference quantization label of the image pair based on the average depth, and taking the natural language text as a depth difference text label, thereby obtaining an image data set. The method can ensure large-scale production and improve sample quality.

The present application relates to the field of electric digital data processing, and particularly relates to a joint training method and system based on real data and synthetic data, the method comprising: obtaining a mixed sample set composed of real data and synthetic data; constructing a global correlation graph based on the mixed sample set, extracting global authenticity and global closeness, and adaptively determining an optimal number of neighbors for synthetic sample quality evaluation; for each synthetic sample, constructing a neighbor subgraph by K-neighbor method using the optimal number of neighbors, extracting subgraph authenticity and subgraph closeness, and obtaining a quality score by a fusion algorithm; giving a fixed weight to real samples and a corresponding quality score to synthetic samples as a weight, constructing a weighted loss function based on sample weights, and completing joint training of the model. The present application improves the model training effect through adaptive number of neighbors, multi-dimensional quality evaluation and weighted differentiated training.

The application provides a perioperative patient sample dataset balancing method and a sample dataset acquisition system. The sample dataset balancing method comprises the following steps: S1, oversampling minority class label samples in a sample dataset of a perioperative patient to obtain synthetic samples, and generating a corresponding synthetic label set for the synthetic samples; the sample dataset comprises a plurality of samples and a sample corresponding classification label set; S2, adding the synthetic samples and the synthetic label set to the sample dataset to obtain a temporary sample dataset; and S3, cleaning the samples in the temporary sample dataset to obtain a balanced sample dataset. The minority class label samples in the sample dataset are oversampled to increase the number of minority class label samples, the majority class label samples and the minority class label samples are balanced, the noise samples are cleaned to improve the sample quality in the output balanced sample dataset, and the balanced sample dataset can improve the performance of a classification model during subsequent classification processing.

The invention relates to the technical field of lead-zinc ore sulfur content determination, and discloses a method for determining the sulfur content in lead-zinc ore and concentrate. The method comprises the following steps: collecting physical and chemical attribute information of a lead-zinc ore or concentrate sample, wherein the information comprises sample quality, particle size distribution and element composition data; generating a fluxing agent proportioning scheme according to the physical and chemical attribute information, wherein the scheme defines the proportional relation of various fluxing agent components; mixing the sample and the fluxing agent according to the proportioning scheme, and putting the mixture into a combustion container; burning the mixture in a high-frequency induction heating mode to generate sulfur-containing gas; measuring a concentration signal of sulfur dioxide in the sulfur-containing gas by adopting an infrared absorption detection technology; and calculating the sulfur content value in the sample based on the concentration signal. According to the method, a fluxing scheme is formulated in combination with sample characteristics, efficient heating and specific detection are matched, the sulfur content is accurately analyzed, and the actual requirements of lead-zinc ore and concentrate detection scenes can be met.

The invention discloses a sample library full life cycle management system based on a block chain, and relates to the technical field of biological sample management, the sample library full life cycle management system comprises a sample management module, an inventory management module, a data analysis module, a knowledge graph module, a follow-up visit management module and an early warning module, the data cannot be tampered and traceability is ensured, and the knowledge graph module adopts a cold start and progressive construction method of a knowledge graph. According to the method, non-tampering recording of the whole process from warehousing to ex-warehousing of the sample is realized through a block chain technology, traceability of the whole life cycle of the sample is ensured, causal analysis and discovery, sample quality prediction, storage period quality prediction and optimal scheme recommendation are realized based on the knowledge graph and multi-dimensional data integration, and the method is suitable for large-scale popularization and application. The method provides data-driven decision support for sample management, and is suitable for medical institutions, scientific research institutions and other scenes requiring standardized and digital management of biological samples.

The present invention discloses a data generation method and device, which relates to the field of data processing technology, in order to solve the problem in related technologies that easily affects sample quality and leads to poor pedestrian re-identification effect. The method includes: obtaining a first pedestrian image sample set, the first pedestrian image sample set having a long-tail distribution; performing feature extraction on each first pedestrian image sample in the first pedestrian image sample set to obtain a first feature vector corresponding to each first pedestrian image sample; decomposing each first feature vector into a first identity component feature and K first change component features; performing weighted fusion on the first identity component feature and the K first change component features after decomposition of each first feature vector to obtain a second feature vector, and the second feature vector is used to generate pedestrian image samples of the tail category. The present invention can achieve a balanced distribution of categories in the pedestrian image sample set without affecting the sample quality, thereby ensuring the pedestrian re-identification effect.

Provided is a moisture meter including a mass measurement unit for measuring the mass of a specimen, a heater for heating the specimen, a control arithmetic unit configured to control the heating unit to heat the specimen until a change in the mass of the specimen becomes not more than a predetermined threshold and calculate the moisture content of the specimen, a timepiece for measuring a heating time, and a storage unit. The control arithmetic unit measures the moisture content of a standard substance for inspection and a heating time, diagnoses the presence / absence of an abnormality in the heating unit by comparing a measured heating time with a predetermined time as a set heating time stored in the storage unit in advance.

The invention discloses a method for testing the content of trivalent metal elements in a metal oxide, and belongs to the technical field of chemical analysis. The method comprises the following steps: firstly, reducing trivalent metal elements in a sample into divalent metal elements by using a reducing agent ammonium ferrous sulfate in a strong acid environment, wherein unreacted ammonium ferrous sulfate remains in a system; then, potassium permanganate is adopted for titration of unreacted ammonium ferrous sulfate, the end point is based on the fact that the solution becomes light pink and keeps color fastness for 15 seconds, and meanwhile a blank solution is set to eliminate system background interference; by comparing the difference between the blank titration volume and the sample titration volume and combining the titration concentration and the sample mass, the content of the trivalent metal elements in the sample can be accurately calculated. The method has the advantages of simplicity and convenience in operation, strong anti-interference capability, high detection precision and wide application range, is suitable for various metal oxide material systems, and is particularly suitable for quality control and performance analysis of new energy materials and catalysts.

The utility model belongs to corrosion sample processing technical field, concretely relates to a bar corrosion sample forming device. Bar corrosion sample forming device, the positioning pile, the positioning plate be located in sealed box, the forming die is fixed on the positioning pile and the positioning plate outside the bar sample and is covered, and vacuum system is connected with sealed box, is provided with vacuum gauge on the connecting pipeline. Bar corrosion sample forming device of the utility model, sectional type detachable mould is convenient for assembling and splitting, and the positioning pile, the positioning plate and the replaceable silica gel bushing can be adapted to various specifications bar sample, and the universality is strong, and the cost is low, the positioning structure guarantees that bar sample is placed vertically, and the scale line and the overflow hole ensure coating accuracy, improve sample quality, vacuum system reduces coating bubble, strengthens stability, is applicable to high -efficient preparation corrosion sample.

The invention discloses a contrast learning recommendation method based on adaptive view optimization, and the method comprises the following steps: enabling an adaptive view enhancement module to generate an enhanced interaction graph from an original user article interaction graph, and calculating adaptive enhancement loss; inputting the original user item interaction diagram and the enhanced interaction diagram into a diagram message transmission encoder module, and performing neighborhood information propagation and node feature fusion to generate node characterization; executing a view alignment task through a view alignment module, fusing node representations, and outputting an aligned node feature matrix; performing final optimization of the recommendation model; and synchronously calculating Bayesian personalized sorting loss and multi-level contrast learning loss, and continuously iterating until the recommendation model converges. By means of a multi-level contrast learning loss-based contrast learning strategy, local negative samples are screened through feature distances, weights are dynamically adjusted, difficult negative samples similar to anchor point samples are emphatically optimized, and the overall sample quality is improved, so that the accuracy of a recommendation result is improved.

The invention relates to the field of detection, in particular to a food detection method which can determine heat conduction lag coefficients of a sample to be detected and a reference substance at previous moments for points corresponding to each target moment on a heat flow curve generated by a differential scanning calorimeter. The method comprises the following steps: determining the actual heat conducted to a to-be-detected sample and a reference substance at a target moment according to the heat quantity of the to-be-detected sample and the reference substance, calculating the actual heat flow to correct a heat flow curve, and judging the quality of the food by using the corrected heat flow curve, so that the hysteresis quality of the heat generated by the heater conducted to the sample and the reference substance is fully considered; the heat flow curve obtained through correction according to the method can better show the actual heat characteristics of the sample, and therefore accurate judgment on the quality of the sample can be achieved.