10results about How to "Accurate quantitative analysis" patented technology

This application discloses a flexible light-emitting sensor, its fabrication method, and its application in the non-destructive detection of mercury ions, belonging to the field of sensor materials and detection technology. The technical solution of this application introduces phycocyanin as a functional crosslinking agent into a polyacrylamide (PAAM) network. The prepared phycocyanin-polyacrylamide (PC-PAAM) hydrogel combines the structural stability of polyacrylamide with the fluorescence properties and chemical reactivity of phycocyanin. This gel, within a soft and biocompatible monomer network, integrates strong responsive fluorescence, intrinsic antioxidant protection, and excellent underwater adhesion, achieving direct fingertip contact surface sensing with a synergistic mechano-optical effect. Furthermore, by combining a machine learning model trained based on hydrogel fluorescence images, it can further support precise, portable, and visualized quantitative analysis of mercury ion pollution using smartphone imaging technology.

The application discloses damaged mitochondria as a malignant tumor metastasis prediction marker, and an application and detection system thereof, and is used for solving the problems of lacking specific prediction indexes and efficient and feasible prediction means for predicting malignant tumor metastasis in the prior art. The application rapidly enriches mitochondria from a to-be-detected plasma sample, detects the concentration of the prediction marker damaged mitochondria, and predicts the malignant tumor metastasis risk of a patient corresponding to the to-be-detected plasma sample based on the concentration of the prediction marker damaged mitochondria, so that whether the tumor patient will have metastasis in the future can be simply, rapidly and accurately predicted.

The application provides a heat storage performance attenuation reason tracing method of a particle energy storage system, and belongs to the technical field of particle energy storage systems.The application uses molecular dynamics simulation to analyze the heat conduction mechanism at the molecular scale and establish an attenuation model, uses a discrete element method to simulate the inter-particle contact heat transfer behavior to construct a particle scale attenuation model, establishes a system scale heat transfer mathematical model based on the continuum mechanics theory, uses the encoder-decoder architecture of the heat conduction prediction model to realize multi-scale coupled heat transfer attenuation prediction, adjusts the model parameters by comparing the measured data and the prediction results, establishes a heat storage performance attenuation reason discrimination matrix and uses recursive feature refinement processing to determine the dominant attenuation mechanism, and formulates a corresponding performance repair strategy according to the attenuation mechanism type, thereby solving the technical problem that the root cause of the heat storage performance attenuation of the particle energy storage system cannot be accurately identified and traced.

The application relates to the technical field of intelligent interaction systems and automobile customer service marketing, and discloses a traffic automobile customer service marketing method and system based on a large language model, wherein the traffic automobile customer service marketing method based on the large language model comprises the following steps: collecting driver fixation point data by using an eye movement tracking algorithm to generate eye movement trajectory data sets; constructing a visual attention heat map to form a user attention distribution model; calculating multi-medium characteristic parameters to establish a medium characteristic model; using the large language model to generate and adapt personalized marketing content and output multi-medium compatible marketing information; realizing intelligent attention guidance according to the user attention distribution model, the medium characteristic model and the multi-medium compatible marketing information; and realizing accurate perception and quantitative analysis of the visual attention of the driver through the eye movement tracking technology, so that the marketing system can master the user focus points in real time, and the technical problem that a traditional interface cannot perceive actual user focus points is solved.

The invention relates to the technical field of biochemical detection, in particular to a sample pretreatment method, a method for detecting the ratio of 5-methylcytosine to cytosine and a kit. The sample pretreatment method comprises the following steps: providing a sample containing substances to be detected, wherein the substances to be detected comprise 5-methylcytosine and cytosine; the method comprises the following steps: mixing magnetic beads containing sulfonic acid groups with a sample, carrying out adsorption treatment, and then carrying out leaching treatment by using an alcoholic solution to obtain a to-be-detected substance-magnetic bead compound; eluting the to-be-detected substance-magnetic bead compound by using the eluent to obtain a to-be-detected sample; the eluent comprises at least one of a formic acid-alcohol solution and an ammonia water-alcohol solution. The sample pretreatment method is simple in process and low in cost, the to-be-detected sample containing the 5-methylcytosine and the cytosine can be efficiently obtained, the ratio of the 5-methylcytosine to the cytosine can be accurately quantified in a high-sensitivity mode on the basis of the electrospray high-efficiency ion mobility spectrometry technology subsequently, and the method has good application prospects.

The application discloses a kind of chip surface submicron dirty spot half-supervised automatic positioning method, comprising: S1, chip surface image is shot;S2, rough positioning: construct double memory bank driven semi-supervised AE defect positioning network, quickly resolve the two-dimensional center coordinates and radius of dirty spot;S3, fine positioning: the rough positioning area of dirty spot is carried out high-density depth data acquisition;S4, dirty spot three-dimensional model construction: the accurate depth value of each dirty spot is fused with the two-dimensional coordinate in rough positioning, and the high-precision point cloud model of micro dirty is generated.The method constructs double memory bank driven semi-supervised AE defect positioning network and based on galvanometer scanning image field telecentric spectral confocal detection system, effectively solve the detection problem caused by the scarcity of micro dirty sample in industrial scene, break through the bottleneck of low efficiency of traditional full-field scanning, realize the efficient, accurate three-dimensional reconstruction and quantitative analysis to the geometry appearance of chip surface submicron dirty.

The invention discloses a hydrogen leakage diagnosis method and device based on multi-modal information combination, and relates to the technical field of industrial safety monitoring and fault diagnosis, and the method comprises the steps: determining whether hydrogen concentration data exceeds a set concentration threshold value or not; if the hydrogen concentration data exceeds a set concentration threshold value, acquiring multi-modal information data in real time; if the hydrogen concentration data does not exceed the set concentration threshold value, processing is not carried out; determining an acoustic confidence map, an optical confidence map and an infrared confidence map; determining a fusion weight parameter based on the environment information data and the memory cell bank, and further obtaining a fusion confidence map and a weighted fusion feature vector; determining a hydrogen leakage position based on the fusion confidence map; adopting a multi-output neural network regression model to obtain hydrogen leakage quantitative parameters based on the weighted fusion feature vectors; and updating the memory cell bank based on the environmental information data, the hydrogen leakage position and the hydrogen leakage quantitative parameter. The accuracy, reliability and long-term stability of hydrogen leakage diagnosis can be improved.

The application discloses an ultrasonic uterine junction zone evaluation system, and the subjective process is converted into objective mathematical calculation through image processing and computer vision algorithm. Specifically, after the image quality is optimized by using anisotropic diffusion filtering and CLAHE algorithm, a hybrid segmentation technology based on edge detection and three-dimensional region growth is adopted to accurately extract a three-dimensional geometric model of JZ. On this basis, the integrity of JZ is quantified by defining and calculating a core parameter, namely, continuity index (CI). The algorithm samples along the center line system of JZ, analyzes the mutation of the local width of adjacent points, and accurately represents the continuity degree of the ring structure by using a mathematical proportion (CI value).

The application provides a CT image-oriented interactive three-dimensional reconstruction and classification method, device and equipment, relates to the technical field of image classification, and the method comprises the following steps: inputting a plurality of second CT images into a general volumetric medical image segmentation model for interactive segmentation, performing three-dimensional reconstruction on a target mask obtained to obtain a first target model; the smoothness of the surface of the first target model is optimized based on a geodesic active contour method, and a target region of a second target model is extracted; volume measurement is performed on the target region to obtain volume data information to classify the plurality of second CT images. The application uses a general volumetric medical image segmentation model to perform interactive segmentation on a plurality of second CT images, in order to improve the accuracy of model calculation, the application optimizes the smoothness of the surface of the first target model based on a geodesic active contour method, and the volume size of a target region representing lesion information is calculated based on the second target model to perform accurate classification.

The invention discloses a medical image segmentation method and system based on a Perturbb-and-Denoise regularization framework, and belongs to the field of medical image processing. According to the invention, the precision and reliability of medical image segmentation can be effectively improved. By performing intelligent disturbance and structural perception denoising on a filter base, the adaptive capacity of the isovariant convolutional network to noise and an image complex structure is enhanced, so that clearer and more continuous segmentation boundaries are obtained on biomedical images with rich details such as electron microscope images and the like. According to the method, the generalization performance of the model under cross-sample and cross-data conditions is improved, meanwhile, the integrity of edges and fine structures is kept, and finally more accurate and robust technical support is provided for quantitative analysis of neuroscience and cellular levels.