9results about How to "Overcoming identification" patented technology

The invention discloses a waste gas degradation treatment effect evaluation method and system, and relates to the technical field of waste gas treatment.The waste gas degradation treatment effect evaluation method comprises the steps that working condition parameters, pollutant concentration and reaction product spectral signals are synchronously collected in the operation process of a waste gas treatment device, and multi-source data are preprocessed to form a time sequence feature set; based on a time sequence feature set, carrying out feature modeling on the spectral signal, extracting main features of a product, comparing the main features with a standard fingerprint database, identifying an abnormal offset degree generated by a by-product, constructing a digital twin model by using working condition parameters, generating an ideal operation state, and calculating a deviation value between reality and ideal; the removal rate is calculated according to the pollutant concentration, the energy efficiency index is generated in combination with the energy consumption parameter, and the removal rate, the energy consumption index, the by-product deviation and the working condition deviation are integrated to form a multi-dimensional operation state vector. The controllability and response speed of the waste gas degradation treatment efficiency are effectively improved, and the intelligent adjusting capacity of the waste gas degradation process is remarkably enhanced.

The invention relates to the technical field of image recognition, and provides a rice field heavy metal pollution risk assessment method and system based on image recognition, and the method comprises the steps: obtaining the multispectral image data of a rice field region; from the multispectral image data, for each pixel point, calculating relative spectral characteristics between selected spectral bands related to heavy metal stress; comparing the relative spectral features with a pre-established heavy metal stress feature mode set to obtain a feature comparison result; the heavy metal stress characteristic mode set is established based on relative spectral characteristic modes corresponding to heavy metal stress observed in different equipment degradation states; and according to a feature comparison result, based on a similarity discrimination threshold and spatial continuity, identifying a heavy metal pollution area and carrying out risk grading so as to obtain corresponding rice field heavy metal pollution risk assessment information. The method has the effect of improving the accuracy of rice field heavy metal pollution risk assessment.

This utility model relates to the field of detector technology, specifically to a glass fiber filament breakage detector, comprising: a base; a steering mechanism including a universal joint, a locking part, and a steering part, wherein the universal joint is fixedly connected to the base, the steering part is rotatably connected to the universal joint, and the locking part is located on one side of the steering part and rotatably connected to the steering part; a support mechanism fixedly connected to the universal joint; and a pneumatic detection mechanism locked to the support mechanism. This utility model solves the problem of detection accuracy attenuation caused by the collection process, ensuring the continuous accuracy of filament breakage detection.

This invention relates to a method, apparatus, equipment, and medium for predicting deformation during the assembly of diesel engine connecting rods, belonging to the field of mechanical assembly technology. The method acquires multi-dimensional assembly data during the assembly process of marine diesel engine connecting rods. Based on assembly process knowledge, the multi-dimensional assembly data is weighted to obtain a weighted input vector set. A pre-trained Bayesian network model is used to infer the deformation prediction probability distribution from the weighted input vector set. Statistical analysis is performed on the deformation prediction probability distribution to obtain deformation trend characteristics and distribution dispersion characteristics. Key deformation risk factors are identified based on these characteristics. A diesel engine connecting rod assembly deformation prediction report is generated based on the deformation prediction probability distribution, deformation trend characteristics, distribution dispersion characteristics, and key deformation risk factors. This invention improves the assembly quality and product reliability of marine diesel engine connecting rods.

The application discloses a kind of based on multi-modal feedback and feature purification ancient building point cloud model completion method, including calculating the local density of each three-dimensional coordinate point in ancient building defect point cloud model, obtain adaptive neighbor quantity, utilize dynamic graph convolution network to process feature descriptor, obtain the component class to which three-dimensional coordinate point belongs;Retain the component class to which the three-dimensional coordinate point corresponding to the component class of classification confidence higher than dynamic confidence threshold belongs, obtain final belonging component class, obtain purified feature based on the class;Construct two-dimensional uniform grid point set, splice purified feature with grid point coordinates in two-dimensional uniform grid point set, obtain high-dimensional feature vector, utilize folding decoder to obtain the component point cloud set after completion, and then obtain the point cloud set after smoothing, utilize multi-modal large language model to process the set, complete point cloud completion.The application realizes high-fidelity fine repair, guarantees the completion stability under full scene.

This invention relates to a machine vision-based method and apparatus for detecting defects in flexible thin films, belonging to the field of flexible thin film detection technology. The method includes acquiring an image of the flexible thin film illuminated by a strip light source; comparing the image with preset pattern features to obtain abnormal regions and their areas; if the area of ​​the abnormal region is greater than a preset area threshold, outputting a preset overprinting processing signal; if the area of ​​the abnormal region is less than the preset area threshold, controlling an active roller to rotate according to a preset instantaneous rotation mode and capturing a group of abnormal images; analyzing the abnormal image group to obtain the jitter characteristics of the abnormal regions; if the jitter characteristics are consistent with preset instantaneous rotation characteristics, outputting a preset stain processing signal; if the jitter characteristics are inconsistent with preset instantaneous rotation characteristics, controlling an adsorption device to adsorb the abnormal regions. This invention effectively alleviates the problem of low efficiency in manual inspection of flexible thin films.

The present application relates to a kind of full-band beat vibration identification methods of rotating equipment, the method comprises: using vibration velocity sensor to collect the vibration velocity signal of rotor;Using variable mode decomposition VMD to decompose vibration velocity signal, obtain multiple intrinsic mode function;The FFT transform of all intrinsic mode functions is obtained, and corresponding frequency spectrum is obtained;While the analytic signal of all intrinsic mode functions is obtained after Hilbert transform, the corresponding amplitude envelope is obtained, and the FFT transform of each amplitude envelope is carried out, and corresponding envelope spectrum is obtained;Envelope spectrum is analyzed, and whether intrinsic mode function can be beat vibration signal is judged, and whether it is beat vibration signal is further determined after analyzing frequency spectrum, and excitation component is obtained.Compared with prior art, the present application can effectively avoid the uncertainty caused by subjective judgment, improve the robustness when tracing analysis, and realize full-band beat vibration detection under the condition of no rotation frequency prior.

The application discloses a kind of full-channel consumer behavior tracking and market survey integrated platform, platform uses federal learning and difference privacy technology to construct privacy-friendly multi-feature identity analysis model, realize the high-precision matching of cross-channel user.The platform uses graph attention neural network to represent the full-channel behavior of consumers as a heterogeneous directed graph, proposes a new structural friction index to identify conversion barrier nodes in the decision-making journey.Using auto-encoder to jointly embed explicit survey data and implicit behavior data, and introducing causal forest model to estimate the causal effect of attitude factors on purchasing behavior, a comprehensive value function is constructed to integrate behavior preferences, attitude tendencies, causal effects and journey resistance.The decision support layer automatically triggers personalized marketing interventions based on the friction index and value score, and dynamically optimizes the model based on A / B test feedback.The application realizes the balance between privacy protection and accurate identification, and can deeply understand the consumer decision-making journey and provide causal-driven value prediction.