15results about How to "Accurate distinction" patented technology

The application discloses a kind of based on artificial intelligence's consumer goods harmful substance intelligent screening method, it is related to artificial intelligence technical field, it is difficult to effectively identify and analyze the technical problem that a large number of natural language, commercial name or commonly known as etc. non-standardized description exists in ingredient list, it is also difficult to accurately convert these fuzzy text information into the standardized terminology that can be accurately matched by regulation database;Including the following steps: the fuzzy ingredient list containing natural language or commercial name description is preprocessed by multi-task text regularization model;Then, utilize embedding semantic similarity model and the multi-evidence fusion presumption unit of improved attention mechanism, fuzzy component is intelligently analyzed and matched, and standard substance identification and confidence are output;Finally, through multi-agent risk decision framework combines real-time regulation database, dynamic compliance screening and risk assessment are carried out;The application improves the intelligent level of consumer goods safety compliance management.

This invention discloses a transgenic recombinant immune cell that specifically targets tumors. The recombinant immune cell comprises a chimeric polypeptide and a chimeric antigen receptor; the chimeric polypeptide includes a first extracellular region, a first transmembrane region, and a first intracellular region with HLA-G protein binding activity, wherein the N-terminus of the first transmembrane region is connected to the C-terminus of the first extracellular region, and the N-terminus of the first intracellular region is connected to the C-terminus of the first transmembrane region; the first transmembrane region includes the transmembrane region of the Notch receptor protein from Xenopus laevis; the antigen-chimeric receptor does not have HLA-G protein binding activity. This recombinant immune cell significantly improves the precise recognition of tumor cells by immune cells, reduces off-target effects of cell therapy, and accurately and effectively distinguishes tumor cells from normal cells, providing an effective target and targeting method for the treatment of broad-spectrum tumors (especially solid tumors).

The application discloses a kind of detection devices of parasitic loop, and the capacitor input module includes at least two parallel capacitors, and each capacitor can be respectively connected to the measured loop or disconnected with the measured loop by different capacitor lines;The host control module is used to detect the voltage of two voltage detection points of the measured loop after the air switch of the measured loop is disconnected, before the capacitor is connected;Control capacitor input module in the capacitor is connected to the measured loop by different capacitor lines in turn, and the voltage of two voltage detection points is detected every time a capacitor is connected;According to the voltage variation of two voltage detection points, it is judged whether the measured loop exists parasitic loop or not.The application can accurately distinguish parasitic series electricity and induced electricity, exclude the interference of induced electricity, truly judge whether detection parasitic loop exists or not, without manual wiring, save time and effort, and can be applied to the parasitic loop detection of various secondary circuits.

This invention relates to the field of network protocol technology, specifically to a data processing method for a multi-protocol fusion monitoring terminal based on edge computing. The method includes: acquiring port static level signals and outputting test pulses; calculating the RC time constant; matching communication parameters to generate a port device identifier; based on the exposure time of video frames, backtracking and linearly interpolating in a circular buffer to generate synchronous sensor values; generating a spatiotemporally aligned fusion data stream and uploading it to the main processor; monitoring heartbeat signals sent by the main processor during the transmission of the fusion data stream; if the heartbeat interval exceeds a preset threshold, determining a data transmission link failure, blocking the normal display path of the fusion data stream, and outputting a preset alarm display interface; simultaneously performing data processing and transmission, capturing port surge current waveforms, extracting energy integral characteristic parameters, and comparing them with a benchmark fingerprint; if the device health status is determined to be abnormal, performing port isolation operations.

This disclosure presents embodiments of a counter-drone reconnaissance method, apparatus, electronic device, and computer-readable medium. One specific implementation of the method includes: extracting object features from millimeter-wave radar data collected against a preset electronic fence airspace to generate an object feature information group; performing radio frequency (RF) signal reconnaissance to generate an object RF information group; identifying object RF information in the object RF information group that meets preset signal filtering conditions as candidate RF information, obtaining a candidate RF information group; performing infrared detection to obtain an infrared detection result group; removing interference from object feature information in the object feature information group that represents non-drone targets to generate a drone feature information group; performing counter-drone jamming based on the drone feature information group and performing counter-drone source tracing to generate a drone reconnaissance result group. This implementation can improve the accuracy of drone target detection.

This invention provides an adaptive high-frequency signal filtering and enhancement method for SAS imaging of subsea pipelines, relating to the field of signal processing. The method includes: performing acoustic propagation compensation on the original high-frequency echo signal; obtaining logarithmic domain distance data through range pulse compression and homomorphic logarithmic transformation; traversing the resolution cells to construct a joint discriminant factor; adaptively determining the global noise threshold set and classifying the resolution cells into noise-dominant regions, edge-textured regions, or flat regions; applying differentiated filtering strategies for different region types for range filtering; and combining the results with inverse exponential transform and azimuth-matched filtering to output an enhanced image. This invention achieves accurate region attribute identification through the joint discriminant factor, and by combining adaptive threshold segmentation and differentiated filtering, effectively suppresses speckle noise while preserving the pipeline edge structure, significantly improving the imaging quality of subsea pipelines.

This invention discloses a method for predicting attribute scattering center parameters based on automatic radar component segmentation and a phase-coordinated network. First, the method utilizes a PointNet++ network with multi-scale grouping and normal vector features to automatically segment complex target point clouds into basic geometric components, and achieves physical diversity of ray data based on the segmentation results. Second, a phase-coordinated network is constructed, endowing the network with electromagnetic interference sensing capabilities through explicit phase encoding. Distributed covariance pooling is used to accurately capture the spatial topological shape distribution of rays. A multi-task decoupled prediction head and a physically constrained sensing loss function are used to achieve high-precision prediction of the attribute scattering center position, field value, amplitude, frequency dependence factor, and length distribution parameters. This invention significantly improves the modeling accuracy and physical consistency of the electromagnetic scattering characteristics of complex targets, and its inference speed is faster than the traditional ESPRIT algorithm, making it suitable for real-time radar target recognition and RCS reconstruction.

This invention relates to a method, apparatus, device, and medium for single-cell feature analysis based on hyperspectral coherent Raman scattering imaging, used for single-cell feature analysis. The invention provides an apparatus for performing hyperspectral coherent Raman scattering imaging of samples at different Raman shifts; the method performs hyperspectral coherent Raman imaging on samples enriched with cells using medium A, acquiring images and Raman spectra of different substances at multiple Raman shifts; a multivariate curvature resolution method is used to obtain concentration distribution maps of different substances, and an image is obtained after removing medium A; deep learning technology is used to segment single-cell regions, extracting morphological and metabolic features of cells; simultaneously, a spectral phasor method combined with Lorentz fitting is used to extract morphological and metabolic features of single cells and their subcellular structures. This invention solves the problem of medium structure occlusion, resulting in richer extracted morphological and metabolic features of cells, facilitating more accurate differentiation of cell types.

This invention provides a data analysis-based method and system for compliant registration and identification of tenants in rental properties, relating to the field of data processing technology. The method includes: mapping each behavioral record in each property identifier to a tenant occupancy marker at each time point; constructing a tenant occupancy continuity relationship between adjacent time points; identifying whether each person is stably residing in the rental property, obtaining an occupancy disturbance value; identifying and separating the portion of a person's continuous trajectory belonging to the registered tenant identifier; splicing together the temporally adjacent portions of different people's continuous trajectories with the same tenant identifier; identifying the duration of each person's presence in the rental property, obtaining an occupancy duration value; and filtering out trajectories from unregistered occupancy trajectories where both the occupancy duration value and the occupancy disturbance value meet a preset stability value, thus obtaining differential tenant trajectories. This invention can accurately identify the actual residents of rental properties, avoiding identification errors caused by delayed or incomplete registration information.

This invention provides a SLAM-based lidar obstacle avoidance and navigation method for a blade interior inspection robot, belonging to the field of radar obstacle avoidance technology. This method acquires the original point cloud of the blade's internal environment and performs intensity compensation correction and sampling density adjustment, adapting to the reflective characteristics of the blade's internal materials and surface geometric changes, thus improving the accuracy of the point cloud data and its environmental characterization capabilities. By mapping the updated point cloud to a polar coordinate system to construct a ring-shaped obstacle density heatmap, it can intuitively reflect the distribution of obstacles around the robot, providing structured perception input for real-time obstacle avoidance. By fusing historical pose sequences with the SLAM map to achieve map correction and dynamically adjusting the obstacle avoidance strategy based on obstacle type, it can achieve more reliable environmental modeling, more accurate obstacle differentiation, and more adaptive navigation and obstacle avoidance control in the complex, unstructured environment inside the blade, thereby improving the autonomous operation safety and operational efficiency of the inspection robot.

This disclosure relates to a device control method, apparatus, device, and readable storage medium, and pertains to the field of intelligent voice technology. It includes: acquiring audio to be recognized, the audio containing voice information; recognizing the audio based on a preset wake-up word recognition model to determine whether the audio contains a preset wake-up word; if the audio contains the preset wake-up word, controlling the device to enter a wake-up state; otherwise, controlling the device to remain in a non-wake-up state. Using the device control method proposed in this disclosure, preset wake-up words and non-preset wake-up words can be accurately distinguished, thereby reducing the false wake-up rate of the device.

This invention relates to a multivariate near-infrared quantitative model based on a one-measure-multiple-evaluation method, its construction method, and its application. The construction method includes the following steps: (1) preparing a representative sample set; (2) using chlorogenic acid as an internal reference, obtaining the content of target polyphenolic compounds in each sample in the representative sample set of step (1) using a one-measure-multiple-evaluation method based on liquid chromatography, and collecting near-infrared diffuse reflectance spectral data of each sample in the representative sample set of step (1); (3) correlating the content of target polyphenolic compounds in each sample in the representative sample set of step (2) with the near-infrared diffuse reflectance spectral data, establishing a regression model and solving it, determining the optimal number of principal components, completing the model construction, and performing model validation. The multivariate near-infrared quantitative model provided by this invention can realize the simultaneous and rapid detection of multiple target polyphenolic compounds in tobacco or its products, adapting to the needs of precise laboratory analysis and large-scale industrial screening.

The embodiment of the invention provides a data processing method and device, electronic equipment and a storage medium, the method is applied to a database system, and a hot storage layer and a cold storage layer are mounted on the database system; the method comprises the steps of obtaining access logs and metadata change logs of N pieces of first data in a first time period; through a pre-trained prediction model, a first probability of each piece of first data is obtained according to the access log and the metadata change log, and the first probability represents a probability that the first data belongs to hot data or cold data; determining hot data and / or cold data in the N pieces of first data according to the first probability of the N pieces of first data; and performing a migration operation on the hot data and / or the cold data to enable the hot data to be stored in the hot storage layer and the cold data to be stored in the cold storage layer. Therefore, according to the embodiment of the invention, the problem that the architecture of the existing database storage system cannot consider both the storage cost and the data query performance can be solved.

The application discloses a surface cover change detection method based on multi-temporal remote sensing images and relates to the technical field of natural resource law enforcement and ecological protection supervision; the method comprises the following steps: obtaining monthly multi-temporal remote sensing images covering 12-24 months, basic geographic information data and ecological function regionalization data and performing pretreatment; performing per-pixel time series decomposition on the standardized images, extracting change starting time, duration, change rate and stage spectral characteristics, and constructing change behavior fingerprints of each pixel; generating to-be-detected change map spots through spatial clustering; performing similarity matching on the change behavior fingerprints of the map spots and a pre-trained standard behavior fingerprint library, identifying surface cover change types; and finally, calculating the severity of illegal activities and evaluating ecological hazards, and generating a dynamic law enforcement report. The application breaks through the static thinking of traditional double-temporal change detection, can automatically identify multiple illegal activity types and evaluate ecological hazards.