436 results about "Sample quality" patented technology

The embodiment of the invention provides an intelligent project text generation method and device, and the method achieves the precise screening of positive and negative training corpora through the construction of a multi-dimensional scoring mechanism, and the comprehensive evaluation of the text coverage degree, the technical correlation degree, the fluency and the professional term specification degree. And constructing a complete knowledge graph based on the relation extraction model and the external knowledge base, and fusing the enterprise technical feature matrix and the basic information to generate an enterprise portrait vector. By transforming a decoding layer structure of a pre-training large model, enterprise portrait vectors are used as condition vectors to be embedded, and personalized text generation is achieved. According to the method, the defects of the traditional technology in the aspects of training sample quality evaluation, knowledge graph integrity and enterprise feature fusion are effectively overcome, and the professionality and pertinence of project text generation are remarkably improved.

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 discloses a generative zero sample learning method based on two-state collaborative decoupling and semantic refining, and belongs to the field of generative zero sample learning. According to the method, static features such as a background, a structure and details of an image are decoupled, and dynamic common features are extracted by a cross-modal label generation module, so that complementary expression of the static and dynamic features is realized; the feature focus is dynamically adjusted according to different confusion types, the feature discrimination is enhanced, and cross-class interference is relieved; on the semantic level, by constructing a vision-semantic mirror image cross attention mechanism, bidirectional alignment between semantic features and visual features is achieved, and the multi-granularity capability and adaptability of semantic representation are further improved. According to the method, feature structure decoupling, confusion adaptive regulation and control and dynamic semantic alignment are taken as the core, the cross-category generalization ability and the generated sample quality are effectively improved, the performance bottleneck of traditional generative zero sample learning is broken through, and the method has high theoretical value and wide application prospects.

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.

Methods and systems for monitoring and / or controlling production units used at various stages of biological product development and manufacturing are presented herein. In particular, in certain embodiments, the biological production monitoring and control techniques described herein utilize a mid-infrared (mid-IR) analyzer that is capable of obtaining mid-infrared spectral data from an aqueous sample in substantially real time. The techniques described herein can utilize this mid-infrared spectroscopic data to measure sample quality metrics, such as protein content, titer, secondary structure, aggregation, and the like, as well as viral and / or nucleic acid characteristics. Thus, sample quality metrics can be measured substantially in real time and / or continuously to assess, for example, the production quality of protein and gene therapeutic agents, and can be used by a control system to adjust and / or control process parameters to improve product quality, etc.

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 the technical field of cloth testing and sampling, in particular to a cloth testing and sampling device which comprises a conveying device used for conveying cloth, the conveying device is composed of a sliding seat, an electric sliding table and a storage table, and a sampling cutter is arranged above the sliding seat; a driving mechanism for driving the sampling cutter is arranged outside the sliding seat; the sampling cutter comprises an annular cutter and a telescopic sleeve, and the annular cutter is telescopically connected with the telescopic sleeve; the driving mechanism comprises a rotary drum, a connecting piece is arranged outside the rotary drum, an abutting piece abutting against the rotary drum is arranged on the connecting piece, and a transmission rod fixed to the upper surface of the telescopic sleeve is arranged outside the rotary drum. The cloth sampling device is high in automation degree, manual intervention is reduced, the sampling efficiency and accuracy are improved, the annular cutter cuts cloth in the rotating and pressing process, it can be guaranteed that the cutting edge is neat, the sample quality is good, and the design of the telescopic sleeve can meet the sampling requirements of cloth with different thicknesses.

The invention discloses a method for evaluating the detection effect of a fusion gene detection method, and belongs to the technical field of evaluation based on the detection effect. According to the invention, a detection reference substance I and a detection reference substance II are constructed, and the detection reference substances are connected with a reference gene segment in equal molecular number proportion, so that the detection condition under the condition of equivalent reference gene expression level can be realized, and whether the sample quality reaches the detection standard or not can be deduced; the measurement of the lowest detection quantity of the fusion gene relative to the expression quantity of the reference gene is realized; the first detection reference is used for detecting sensitivity analysis of the fusion type by the detection method of the fusion gene to be detected, and the second detection reference is used for verifying whether a detection result of the detection method of the fusion gene to be detected is a false negative result or not. And the method has a very good application prospect.

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 discloses an intelligent spirulina quality detection system and method, and belongs to the field of spirulina detection. The invention discloses an intelligent spirulina quality detection system and method. The system comprises a data collection unit, a model construction unit, a model application unit, a model updating unit and a result output unit. The method solves the problem that a large amount of time, manpower and material resources are consumed in the existing detection technology, the spirulina image database can be constructed by collecting the spirulina images and performing processing, labeling, feature extraction, classification, screening and induction, then the intelligent detection model of the spirulina quality is established by using the deep learning technology, and the detection accuracy is improved. According to the method, the quality grade of a sample can be analyzed and judged in real time, an intelligent detection model can be updated and optimized by regularly collecting new image data so as to improve the precision of the model, finally, a detection result is recorded, a spirulina sample quality file is formed, and early warning information is issued when the quality problem of the spirulina sample is found. Therefore, the condition of the spirulina sample can be known.

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 discloses an analysis method for detecting the correlation of moxa quality based on a terahertz metamaterial sensor, and the method comprises the steps: optimizing the structural design and simulation of a polarization-dependent and polarization-independent metamaterial sensor through the CST Studio # imgabs0 # 2019 software by utilizing the resonance characteristic of a metamaterial and an equivalent circuit theoretical model, and determining the resonance peak frequency of the metamaterial sensor. According to the method, the influence of analytes with different thicknesses and refractive indexes on the harmonic peak frequency shift of the sensor is simulated and analyzed, and the relationship between the moxa sample quality and the harmonic peak frequency shift is researched. Compared with a traditional terahertz detection technology, the accuracy and the sensitivity are remarkably improved by combining the designed metamaterial sensor, the sample detection amount is greatly reduced to 60 micrograms from 5 milligrams, and efficient detection of trace moxa is achieved. The innovative scheme provides efficient and accurate technical support for traditional Chinese medicine quality monitoring, and has important practical application value.

In a method for training an entity recognition model, sample text data including entity text content is acquired. Entity recognition is performed on the sample text data using a candidate entity recognition model to obtain an entity recognition result corresponding to the sample text data. A recognition loss value is determined based on a difference between the entity division label and the entity recognition result. A sample quality score corresponding to the sample text data is acquired. Loss adjustment is performed on the recognition loss value based on the sample quality score to obtain a predicted loss value. The candidate entity recognition model is trained based on the predicted loss value to obtain a trained entity recognition model that is configured to perform entity recognition on inputted text data. Apparatus and non-transitory computer-readable storage medium counterparts are also contemplated.

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 belongs to the technical field of medical apparatus and instruments, and particularly relates to a gynecological endometrium sampling device which comprises a liquid negative pressure extraction mechanism, a liquid collection and storage mechanism and a self-adaptive uterus sampling mechanism, the liquid collection and storage mechanism is fixedly arranged on the liquid negative pressure extraction mechanism, and the self-adaptive uterus sampling mechanism is fixedly arranged on the liquid negative pressure extraction mechanism. The self-adaptive uterus sampling mechanism is arranged in the liquid negative pressure extraction mechanism and is in sliding connection with the liquid negative pressure extraction mechanism; liquid in the uterus can be sucked and removed firstly, then comprehensive sampling is conducted, a sampled sample is protected and stored, the sample quality is effectively improved, and the sampling failure problem is avoided.

The invention discloses a biological characteristic sample quality threshold determination method and device and a biological characteristic recognition method and device, and belongs to the field of biological characteristic recognition. According to the method, the mismatching rate is counted under the set mismatching rate level and different discarding rates, the relation between discarded low-quality biological characteristic sample pairs and the biological characteristic recognition performance is established, namely, the correlation curve of the discarding rate and the mismatching rate is established, and the sample quality threshold value under the set mismatching rate level is obtained according to the correlation curve. And a method for quantifying the quality intensity by the biological characteristic sample is provided. The sample quality threshold value is determined according to the parameters of the biological feature recognition performance and is related to the mismatching rate and the mismatching rate, and compared with a manually set sample quality threshold value, the sample quality threshold value has better reasonability and reliability.

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 utility model discloses a wild animal fresh excrement sample collection and storage device, which comprises a box body, a collection tool, a sampling label device and a controller, the box body is provided with a storage chamber and a refrigeration chamber, the storage chamber is used for placing the collection tool, and the refrigeration chamber is internally provided with a refrigeration system and a temperature sensor; the collection tool comprises a disposable glove, a collection spoon, a disposable collection sleeve and a cryopreservation tube; the sampling label device comprises a mobile device and a label printer. The controller is provided with an interface module, a data storage module, a data management module, a label generation module, a printing control module, a GPS module and a temperature control module. The device is convenient to carry and operate, the freshness and stability of the sample are kept through low-temperature preservation of the sample, the sample quality is ensured, the sample information is accurately recorded through the sampling label system, follow-up analysis and statistics are facilitated, the fresh faeces sample of the wild animals is conveniently collected and stored, and the field research efficiency and the data quality are improved.

The invention discloses an iron ore composition detection method and system based on machine learning, and relates to the technical field of data processing.The method comprises the steps that iron ore feature information of target iron ore to be detected is collected, and a plurality of iron ore samples are collected; performing drying and grinding parameter optimization according to the iron ore characteristic information, drying and grinding the plurality of iron ore samples, and collecting the particle size and mass in each drying and grinding process to obtain a plurality of particle size sequence sets and a plurality of mass sequences; preparing a plurality of detection samples, and performing sample quality analysis according to the optimized drying and grinding parameters, the plurality of particle size sequence sets and the plurality of quality sequences to obtain a plurality of sample quality coefficients; and carrying out X-ray fluorescence spectrum detection on the plurality of detection samples to obtain a plurality of spectrum detection results, and carrying out component identification on the plurality of spectrum detection results according to the plurality of sample mass coefficients to obtain an iron ore component detection result. The technical problem of poor iron ore component detection accuracy in the prior art is solved.

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 present invention belongs to the field of coal sample quality monitoring, and discloses a sampling control method, system, computer device and storage medium for a coal carriage of an automobile, including: obtaining the weight of the coal in the current carriage and the carriage size information; obtaining the sampling number of the current carriage according to the weight of the coal in the current carriage and the preset sampling number per unit weight of the coal belonging to the current batch; obtaining each sampling coordinate when sampling according to the sampling number of the current carriage and the carriage size information; converting each sampling coordinate into each virtual sampling coordinate under the standard vehicle type size in turn according to the carriage size information and the preset standard vehicle type size information; obtaining the distribution characteristics of each virtual sampling coordinate, and determining whether there is a risk of sampling fraud according to the distribution characteristics of each virtual sampling coordinate. The problem of sampling representativeness is solved, uniform sampling of the entire coal batch is realized, the phenomenon that large vehicles pull low calorific value coal and small vehicles pull high calorific value coal is effectively prevented, and thus the quality of the entire batch of coal is effectively guaranteed.

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.