521 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 discloses a large-scale sample storage management method and system based on a multi-dimensional label system. The method comprises the following steps: acquiring large-scale sample data input, and establishing a multi-dimensional label system for each piece of sample data based on a sample source, sample quality and sample feature dimensions; performing automatic auxiliary identification and classification and quality evaluation on the sample data, generating a multi-dimensional label for the sample according to a multi-dimensional label system, selecting a proper storage layer according to the multi-dimensional label of the sample, and storing the sample in a specified storage layer after the storage space is optimized; and returning a retrieval result according to a retrieval condition input by a user, and distributing the retrieved sample to a required task or analysis scene according to a requirement. According to the method, by establishing the multi-dimensional metadata labels, retrieval, positioning and management of the samples can be completed quickly and efficiently. Meanwhile, automatic auxiliary classification and recognition of the samples are achieved, and the data management efficiency is 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.

This application discloses a rapid detection method for screening of dwarfism in children, which belongs to the field of intelligent medical technology. It includes the following steps: Step 1, accurately sampling through capillary effect, and automatically diluting the sample to ensure accurate concentration; Step 2, introducing the collected sample into a multi-channel microfluidic detection chip to achieve parallel quantitative detection of endocrine hormones; Step 3, real-time acquisition and amplification of weak biomolecule recognition signals to effectively improve the signal-to-noise ratio; Step 4, multi-dimensional analysis of the test results to achieve quantitative evaluation of growth and development, body mass index and sexual development indicators and risk prediction of endocrine diseases; Step 5, automatically integrating the analysis results and generating a standardized test report; Step 6, establishing a quality control mechanism throughout the entire detection process, including real-time monitoring of sample quality, tracking of detection process parameters, result consistency evaluation, and full traceability of detection data.

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 belongs to the technical field of biological liquid sample detection, and particularly relates to a liquid path system for sample suction quality detection and an anomaly detection process. A liquid path system for sample suction quality detection comprises a capacitance detection module, an injector, a pressure detection module, a cleaning valve, a cleaning pump, a degassing module and a water tank which are connected in sequence, and the capacitance detection module, the injector, the pressure detection module, the cleaning valve, the cleaning pump, the degassing module and the water tank are connected through pipelines. The method mainly solves the following problems: 1, the contradiction between the pressure monitoring range and the monitoring resolution; 2, when the sample is insufficient or bubbles are sucked, the difference between the suction pressure and the normal liquid suction pressure is small, so that misjudgment is easily caused; 3, misjudgment is easily caused by noise signal interference; 4, corresponding error monitoring mechanisms are lacked in other processes such as cleaning, sample adding and the like; and 5, monitoring is performed by excessively depending on a single mode, and the pressure is easily interfered in the testing process.

The invention discloses a seabed sediment classification method based on multilevel comparative learning and uncertainty measurement. The method comprises the following specific steps: dividing target backscattering intensity data to obtain a training set containing marked samples and unmarked samples and a test set containing marked samples; constructing a feature extraction model, and obtaining a multi-level fusion feature based on the training set; obtaining optimized feature representation based on multi-level comparative learning; screening based on the unmarked samples in the training set and the uncertainty measurement to obtain a target pseudo-tag sample; and carrying out joint learning based on the labeled samples in the training set and the target pseudo-label samples. According to the method, a high-quality pseudo label, namely a target pseudo label sample, is screened out through a pseudo label screening strategy based on uncertainty measurement, and the quality of the target pseudo label sample and the adaptability of model learning are ensured; and supervised and semi-supervised joint learning is carried out through the marked samples and the target pseudo-tag samples in the training set, unmarked samples are fully utilized, and the substrate classification performance is improved.

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 relates to a semi-supervised remote sensing image change detection method based on adaptive feature disturbance, which comprises the following steps of: training a teacher model by adopting a weak enhancement training set, and obtaining a prediction deterministic value through maximum value normalization processing to relieve adverse effects caused by unreliability of a pseudo tag; then, a self-adaptive feature disturbance scheme is customized based on each prediction deterministic value, disturbance of sample features can be dynamically adjusted according to sample quality, and controllability is increased on the basis of random disturbance, so that confirmation deviation of a student model is remarkably reduced, and the performance of the student model for semi-supervised change detection of remote sensing image changes is improved.

The invention discloses a urine sampling device for the endocrinology department and a sampling method, and belongs to the technical field of medical analysis. Comprising the following modules: an automatic sampling control module for realizing automatic control of the whole urine sampling process, ensuring that sampling accords with a standard process, and supporting automatic sub-packaging and storage management of samples; the sample quality control module is used for monitoring the physical characteristics of the samples in real time, automatically evaluating the effectiveness of the samples in combination with environmental parameter monitoring, and identifying and marking abnormal samples; the data acquisition and processing module is used for comprehensively acquiring physiological data, nutrition metabolism data, operation process data and sample tracing information, and performing cleaning, standardization and fusion processing to generate a standardized data set; the intelligent diagnosis and analysis module is used for intelligently detecting abnormal physiological indexes and carrying out disease risk prediction and early warning; and the man-machine interaction module provides a visual operation interface and integrates multi-level user authority management, automatic report generation and remote monitoring.

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 artificial intelligence, and discloses an environmental sound classification method and device, computer equipment and a storage medium in the fields of financial science and technology and medical health. The environmental sound classification method comprises the following steps: acquiring real-time environmental sound data, and preprocessing the real-time environmental sound data to obtain a sound sample to be analyzed; performing data analysis processing on the to-be-analyzed sound sample through a preset classification model to obtain a sound classification result corresponding to the real-time environment sound data; the preset classification model is obtained by training an initial classification model through an initial sound sample and a synthetic sound sample, the synthetic sound sample is obtained by performing data synthesis processing on the initial sound sample through a preset synthesis model, and the preset synthesis model comprises a generative adversarial network and a diffusion model network. According to the method, the data processing efficiency and the sample quality of model training can be improved, the generalization ability and robustness of the model are improved, and the accuracy and stability of environmental sound classification are improved.

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 analyzing commodity package inspection based on an AI algorithm and video acquisition, and the method comprises the steps: analyzing the hole, height and crack of each surface of a detection sample to obtain a hole indication value, a roughness indication value and a crack indication value, and integrating the three indication values in a mode of setting a weight influence factor to obtain a detection result. Therefore, the defect evaluation index of the detected sample is obtained, and the comprehensive analysis of the quality of the detected sample is realized. Further analyzing the defect evaluation index of each detection sample on the same assembly line so as to obtain the production index of each assembly line, triggering analysis early warning and a specific analysis instruction by setting a threshold value and comparing the threshold values, formulating a corresponding processing scheme for each device for producing the detection samples, therefore, factory production can be guaranteed, and production efficiency is improved.

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 image processing, and particularly relates to an image processing method, terminal equipment and a computer readable storage medium, and the method comprises the steps: obtaining a first image; wherein the first image comprises a vehicle and does not comprise a lane line; acquiring a second image including a lane line; wherein the lane line in the second image is marked; generating a first sample image according to the first image and the second image; wherein the first sample image comprises a vehicle and a lane line, and the marking information of the lane line in the first sample image is consistent with the marking information of the lane line in the second image; the first sample image is used for training a lane line detection model. In the embodiment of the invention, according to the combination of the first image comprising the vehicle and the second image marked with the lane line, the generated sample image comprises the vehicle and the marked lane line, so that the sample quality is effectively improved, and the training precision of the detection model is 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 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.