15 results about "Identification scheme" patented technology

This invention discloses a method, system, and medium for measuring stress and earth pressure behind retaining walls / piles under localized overload. Addressing the shortcomings of existing technologies in simultaneously considering stress or earth pressure measurement under localized overload and rotational displacement of retaining walls / piles, this invention first solves the problem of measuring additional vertical stress behind retaining walls / piles under localized overload. The calculation model is constructed by combining theoretical derivation of the mechanical response of a semi-infinite elastic body surface subjected to a concentrated normal force with experimental test data analysis. Secondly, the invention solves the problem of measuring earth pressure behind retaining walls / piles under localized overload. The optimized scheme provides identification schemes for eight measurement conditions under five types of rotational displacement modes of retaining walls / piles, as well as non-limit active earth pressure measurement schemes under each measurement condition. The measurement method of this invention has a clear calculation model, easily obtainable parameters, and high engineering applicability. This invention also provides a force measurement system based on the measurement method and a medium for carrying the measurement method.

This invention discloses a method and system for automated generation and optimization of database table structures. The method specifically includes: receiving the user's backend project source code; extracting a semantic information network graph related to data persistence through static code scanning; inputting the semantic information network graph into an intelligent parsing engine to analyze code logic relationships and obtain index recommendation strategies and potential table structure design flaws; based on the index recommendation strategies and potential table structure design flaws, identifying sensitive fields and recommending encryption or de-identification schemes to obtain a first table structure; performing automated stress testing on the first table structure and adjusting the index strategy and partitioning scheme based on the test results; and optimizing the first table structure according to the adjusted index strategy and partitioning scheme to obtain a second table structure. This invention achieves intelligent generation and optimization of database table structures, improving development efficiency, enhancing maintainability, improving user experience, and ensuring data security.

The application discloses a geological anomaly body rapid identification method and system based on seismic exploration, and relates to the field of geological anomaly body identification.The method comprises the following steps: fine imaging based on historical seismic data, generation of a high-precision three-dimensional geological model, and construction of a geological regional risk distribution map; emergency evaluation of anomaly body identification, configuration of a plurality of adaptive identification time limits; prediction of the predicted seismic source signal sequence and the predicted noise signal sequence of the second geological region based on the historical seismic source signal sequence and the historical noise signal sequence, analysis of the anomaly body identification complexity, and output of the second optimal identification time limit; optimization of the identification strategy of the geological anomaly body, execution of the geological anomaly body identification of the second geological region, iterative optimization of the identification scheme, and completion of the coal mining task.The geological anomaly body rapid identification method and system solve the problem that the geological anomaly body identification strategy cannot be dynamically adjusted, resulting in low safety warning responsiveness and insufficient accuracy.

This application provides a method, apparatus, device, and medium for identifying and processing recyclable materials. In this method, image data and weight data of the item to be recycled are acquired; the image data are input into a first recognition model and a second recognition model respectively, and inference is performed in parallel to output corresponding material identification results and product identification results; based on the material identification results, product identification results, and weight data, it is determined whether the recyclable item is a qualified recyclable; if it is determined to be a qualified recyclable, a first control command is output, or if it is determined to be an unqualified recyclable, a second control command is output. This solution addresses the problem in related technologies where recyclable material identification schemes cannot meet the requirement of acquiring directly recyclable aluminum cans and determining their product attributes, thus affecting the recycling effect.

The application provides a long-tail distribution object identification scheme fusing an unsupervised domain adaptation technology, comprising an object identification main system, wherein the object identification main system comprises a domain adaptation system, an object library and a model center; the domain adaptation system comprises a prototype level contrast learning and a consistency loss based on a smoothing label; the object library comprises an object image acquisition device and a database; and the model center comprises an object pre-training model and a long-tail identification model. The long-tail distribution object identification scheme can make the photographing object identification module in a shopping / science popularization application program more accurately identify objects in a natural life scene.

The application discloses a robot parameter identification method and system based on a temperature load extended friction model, and relates to the technical field of robot dynamics modeling and control. The method comprises the following steps: optimizing a friction separation path through designing a strict constraint excitation trajectory, collecting experimental data under different temperature load conditions through robot joint friction characteristic testing based on the friction separation path; constructing a Stribeck friction model, performing temperature and load polynomial processing on key parameters, and obtaining a Stribeck effect extended friction model based on temperature, load and speed; and adopting a hierarchical identification scheme of temperature first and then load, and using the Stribeck effect extended friction model of temperature, load and speed to identify robot joint friction parameters from the experimental data. The application realizes accurate identification of robot joint friction characteristics and parameters under variable temperature and variable load combined conditions.

The application provides a game ornament rental management platform and method, and belongs to the technical field of rental management, and specifically comprises the following steps: determining a user portrait of a user based on access data of the user, determining an access management strategy of the user according to the association between the user portrait and different target heat type game ornaments and in combination with the listing data of the game ornaments, performing access restriction processing of the user by using the access management strategy, and determining an identification scheme of an access optimization strategy of a target type user according to the access restriction data of different users and the change of the listing data of the target heat type game ornaments associated with the user portrait of the target type user, so that the rental use experience of the user is improved.

The application discloses a kind of multi-modal tumor identification method and system based on riemannian manifold fusion, belong to tumor identification technical field, to solve the problem that existing multi-modal tumor identification scheme exists spatial and temporal heterogeneous deviation, weak anti-interference ability, image and impedance data deep physical coupling is insufficient, identification conflict lacks effective arbitration;The application first obtains the image and impedance multi-modal original data of space-time synchronization, is completed standardization preprocessing by hardware level clock alignment and cross-modal space coordinate mapping;Again, based on dynamic rhythmization feature distribution, image recognition is completed, impedance recognition is completed based on fluid physical convection and diffusion mechanism, and the corresponding identification result is respectively output;Finally, through the conflict arbitration mechanism of riemannian manifold space mapping and curvature guidance, the dual-mode identification result is fused, and the final tumor identification result is output.The application effectively eliminates the spatial and temporal heterogeneous deviation of multi-modal data, improves the robustness and anti-interference ability of feature extraction in complex clinical environment, realizes the deep physical cooperation of dual-mode data, improves the accuracy and decision fault tolerance of early tumor identification.

This application discloses a method for parts identification. The method first acquires image information of the part to be identified and extracts corresponding part information. Then, it uses this information to query a preset electronic parts catalog. Based on the query results, it flexibly uses an object storage service image library to match candidate part images. Finally, it determines the target part based on the degree of matching between the candidate part information and the part information to be identified. This method integrates the advantages of accurate catalog querying and image visual matching, overcoming the shortcomings of single identification schemes. It comprehensively mines key part information to reduce misjudgments, uses layered processing to balance recognition efficiency and adaptability to complex scenarios, reduces the mismatch rate of similar parts, and quantifies matching judgments to improve the accuracy of results. This achieves efficient and accurate parts identification, meeting the high reliability and efficiency requirements of the automotive aftermarket, reducing user time costs, and improving business transaction efficiency and data update timeliness.

The application discloses a lane line detection model inference optimization method trained by generalization data, and the main design concept of the application is that a lane line identification scheme of association learning+time sequence control is proposed based on a deep learning vision model. Specifically, in the training stage, the vision generation model is used to repair abnormal lane line pictures and predict rare or difficult-to-catch lane line pictures, so that the accuracy and efficiency of lane line labeling are improved in a man-machine coupling manner; in the actual inference stage, the time sequence concept is introduced, the mechanism for evaluating the detection result is proposed by combining the time sequence information of the front and rear frames. The application effectively enhances and generalizes the training data of the detection model, and improves the robustness and accuracy of the lane line detection result, so that many problems of lane line identification in automatic driving application can be solved.

This invention discloses a DNS covert tunnel identification method based on a pre-trained model. The proposed DNS covert tunnel identification scheme based on a pre-trained model mainly consists of three parts: data preprocessing, model pre-training, and model fine-tuning. The data preprocessing stage requires filtering out DNS traffic and converting it into a structured, tokenized format for model pre-training. In the pre-training stage, facing large-scale unlabeled DNS traffic data, the pre-trained model learns a general DNS traffic representation through two pre-training tasks. The fine-tuning stage focuses on fine-tuning the pre-trained model on a small amount of labeled data to detect DNS tunnel traffic and identify specific DNS tunneling tools.

The application discloses a SAR marine oil spill intelligent identification method and system based on a diffusion model, which converts oil spill detection into a conditional generation task, comprising: performing geometric correction, radiation calibration, noise suppression and normalization on a SAR image; adopting a feature confidence modeling module CEM to enhance the reliability of features through spatial and channel double confidence calculation; constructing a conditional diffusion model to guide by taking the SAR image as a condition, and iteratively denoising to generate a segmentation mask by using a noise estimation network of a U-Net architecture; performing five independent generations, and improving stability by pixel-level average fusion and uncertainty quantification; extracting oil spill area parameters through morphological processing, and outputting in a standard geographic information format. The system first applies a conditional denoising diffusion probability model to the marine remote sensing field, realizes pixel-level uncertainty estimation, is significantly superior to existing methods, and provides an intelligent identification scheme with high precision and high reliability for oil spill emergency response.

The present application relates to the technical field of chronic disease health management, in particular to a chronic disease health data management system based on a cloud platform, comprising: collecting physiological index time series data, medication compliance records and text of subjective feelings of signs through a data collection module in combination with a user terminal device and active reporting; a state analysis module reconstructs the multi-dimensional health state of the data set to generate accurate patient health state characteristics; a scheme generation module calls a cloud platform optimization model to generate individualized health management schemes and high-risk warning signs; a scheme correction module corrects the scheme in combination with subjective feelings of signs and historical behavior patterns to improve adaptability; and a warning intervention module generates medication reminders, health monitoring suggestions and other instructions. The system can accurately capture patient health dynamics, improve the executability of management schemes, and achieve precise and individualized health management for patients with chronic diseases.

This invention relates to the field of tunnel ventilation fan fault identification technology, specifically disclosing an adaptive identification method, device, and storage medium for composite faults in tunnel ventilation fans. The method includes: acquiring multi-dimensional sensor data of the tunnel ventilation fan; performing environmental noise processing and speed interference processing on the multi-dimensional sensor data to obtain processed data; determining the general characteristics and operating condition characteristics of the tunnel ventilation fan based on the processed data; performing feature decoupling operations based on the general characteristics and operating condition characteristics to obtain feature vectors; and performing an adaptive composite fault discrimination operation based on the feature vectors to generate a composite fault identification result. By improving traditional ventilation fan fault identification methods, and adopting targeted identification schemes based on the actual operating conditions, environmental conditions, and fault characteristics of the tunnel ventilation fan, the accuracy and reliability of fault identification are improved.

The application discloses a data processing method, a storage medium and an electronic device. The method comprises the following steps: acquiring multi-modal data; performing feature extraction processing on the multi-modal data to obtain first comparison features of a to-be-compared sequence, wherein the to-be-compared sequence is a target object tracking sequence acquired by an image acquisition device; selecting a plurality of clusters based on the first comparison features to obtain second comparison features of each cluster in the plurality of clusters, wherein each cluster in the plurality of clusters comprises at least one sequence belonging to the same target object; performing first similarity comparison processing on the first comparison features and the second comparison features to obtain a first comparison result; and merging the to-be-compared sequence to a target cluster according to the first comparison result. The application solves the technical problems that the existing target object re-identification scheme cannot realize accurate target object association of cross-border heads and real-time merging of target object data.