11results about How to "Effective identification" patented technology

This invention discloses a method and system for identifying innovative points in academic papers, relating to the field of artificial intelligence technology. The method first processes the paper abstract through a concept path extraction module, including: structured semantic segmentation, concept pair extraction and verification, constraint relation triple generation, hierarchical verification, and path optimization, to construct a complete set of concept paths for the paper. Then, in a rare path discovery module, the popularity index of each concept path in the global path frequency dictionary is calculated and compared with a threshold to identify rare paths as the scientific innovative points of the paper. This invention effectively suppresses the illusion of language models through a knowledge graph constraint mechanism, significantly improving the accuracy of concept path extraction and its coverage of long-tail concepts, achieving accurate and efficient identification of innovative points in academic papers at the path level.

This invention relates to the field of digital archives management technology, specifically to an artificial intelligence-based system for analyzing and warning of abnormal archive behavior. The invention uses a graph construction module to transform archive access logs into a relational topology graph; an attribute propagation module performs weighted transfer and accumulation of weights based on a graph traversal algorithm to calculate the cumulative relational index value of nodes; a connectivity retrieval module retrieves cross-regional links based on partition isolation rules; and an early warning output module generates early warning signals based on index values ​​and link status. This invention utilizes the flow mechanism of feature values ​​in a graph structure to aggregate minute risky behaviors within discrete long-term windows, accurately identifying complex abnormal patterns where a single operation may not violate regulations but the long-term cumulative risk exceeds the limit. This solves the problem that existing static statistical rules are unable to detect hidden logical violations, effectively reducing false alarms and false negatives.

This invention discloses a method, system, and device for detecting weak seismic signals based on instantaneous phase analysis, relating to the field of reservoir prediction. The method includes: 1. Inputting a three-dimensional seismic data volume; 2. Extracting the amplitude data of the current seismic trace and recording it as a one-dimensional array; 3. Performing a -90° phase shift on the one-dimensional array to obtain a phase shift array; 4. Performing a time-frequency continuous wavelet transform on the phase shift array to obtain multiple common-frequency amplitude arrays; 5. Performing a Hilbert transform on each common-frequency amplitude array to obtain the corresponding instantaneous phase array; 6. Calculating the second derivative of each instantaneous phase array with respect to the time variable n to obtain a three-dimensional data volume indicating weak signals at the current seismic trace's common frequency; 7. Changing the seismic trace and repeating steps 2-6 to obtain multiple three-dimensional data volumes indicating weak signals at the common frequency; 8. Selecting the optimal weak signal indicator data volume. This invention can effectively find the three-dimensional data volume indicating weak signals, improving the accuracy of detection.

The application relates to the technical field of intelligent sensors, in particular to a high-temperature alloy defect intelligent detection system based on deep learning, which comprises the following steps: obtaining a CAD model of a blade to be detected, a linear attenuation coefficient and crystallization temperature field data, constructing a three-dimensional equivalent thickness matrix and a stress distribution probability field, utilizing a multi-agent reinforcement learning model to adaptively decide X-ray energy, detection gain and infrared excitation frequency, realizing deep adaptation of detection parameters and non-uniform attenuation characteristics of components, constructing a physical reference gray field based on the Beer-Lambert law, stripping a complex geometric structure background and extracting a defect sensitive area through residual operation, synchronously fusing an infrared temperature rise abnormal signal, and physically removing grain structure noise of a single crystal structure in combination with a pixel shift law under microbeam deflection, so that accurate and quantitative output of a defect type, three-dimensional coordinates and size is finally realized. Through multimodal fusion and adaptive decision, the application realizes accurate determination and quantification of defects of complex components.

This invention belongs to the field of quality inspection technology and provides a method and system for nonwoven fabric quality inspection based on image recognition and AI analysis. It obtains a steady-state image by preprocessing the target grayscale image of the nonwoven fabric surface and performs edge detection to obtain multiple grayscale sub-regions. Then, multiple clustering domains are constructed based on grayscale primitives. The similarity fitting degree of all clustering domains is calculated, and abnormal clustering domains are screened out. Finally, the connectivity feature value between the abnormal clustering domain and adjacent clustering domains is calculated, and the presence of fiber nodules in the abnormal clustering domain is determined based on this feature value. This method can quickly and accurately locate areas that may have defects, reduce manual intervention, and improve the automation and accuracy of inspection. Therefore, while ensuring product quality, it also improves production efficiency and avoids potential quality problems.

The application discloses a feature automatic extraction method around a tooth boundary and a lesion recognition method, relates to the technical field of oral medicine, and comprises the following steps: acquiring a digital image of an oral X-ray apical film to be processed; performing automatic segmentation and contour extraction on a target tooth in the digital image of the oral X-ray apical film to obtain a tooth contour line of the target tooth; performing normalization processing on the tooth contour line; and traversing each boundary pixel point on the tooth contour line to generate an edge band feature map of the target tooth. The application can effectively extract local features highly related to lesions, especially features of a tooth boundary region, from the oral X-ray apical film, so that the recognition capability for early micro-lesions is improved.

The invention belongs to the field of image region segmentation, and particularly relates to an AI-based colorectal tumor image region segmentation method and system, and the method comprises the following steps: S1, obtaining original 3D colorectal tumor image data; according to the method, the high-quality 3D colorectal tumor image data is processed through the surface parameterization technology, the obtained 2D feature map is more suitable for processing the hybrid FMU-attention network model, and the processing efficiency and the processing speed of the hybrid FMU-attention network model are greatly improved; in addition, an AI framework is formed by jointly fusing a hybrid FMU-attention network model, a Vision Transformers module and a DeepLabV3 + model, the AI framework enables the boundary to be more accurate and the multi-scale performance to be more excellent, and it is ensured that the tumor can be effectively recognized and segmented regardless of the size.

This invention provides a nanoformulation capable of enhancing the immune response to in situ tumor vaccines. The nanoformulation comprises zoledronic acid, all-trans retinoic acid, and metal ions. Preferably, the metal ion is a lanthanide metal, such as Gd. 3+ Preferred, zoledronic acid: Gd 3+ The mass ratio of all-trans retinoic acid is 4:1:5. The nano-formulation consists of zoledronic acid and Gd... 3+ Zol / Gd-NPs nanocoagulation polymers were formed by mixing in water. All-trans retinoic acid was dissolved in anhydrous ethanol to prepare albumin-encapsulated nanoparticles (A-NPs), which were then mixed with the Zol / Gd-NPs nanocoagulation polymers and sonicated to obtain the final product. These nanoparticles, on the one hand, can utilize zoledronic acid to eliminate macrophages, thereby reversing the inhibition of intratumoral dendritic cells (DCs) by the tumor immunosuppressive microenvironment; on the other hand, all-trans retinoic acid can significantly increase the expression of MHC-I and tumor antigens in tumor cells, thus ensuring the activation of CD8. + T cells can effectively recognize and kill tumors, generating a systemic anti-tumor immune response.

This invention discloses an adversarial hint copyright verification method and device based on multi-model joint gradient optimization. Addressing the weaknesses of single-model fingerprint generation in terms of weak adversarial capabilities, poor transferability, and insufficient concealment, this invention constructs multiple downstream models to simulate technical variations of the original model series. It employs multi-model joint gradient optimization to generate adversarial hints with cross-model recognition capabilities as model fingerprint features. Copyright verification is achieved by detecting the specific response patterns of the test model to the adversarial hints. This invention relies on multiple downstream models to capture the most essential features of the original model. Adversarial hints optimized using these features possess stronger specificity and effectiveness, effectively addressing intellectual property risks such as model leakage and unauthorized distribution.

The application relates to a multi-component ore content soft measurement method based on a DBSCAN-PF-LSTM, which comprises the following steps: extracting historical assay data of multi-component ore content to be measured to establish a multi-dimensional data sequence; adopting a DBSCAN model to identify normal data and abnormal data in the multi-dimensional data sequence, calculating the average value of previous historical assay data of the abnormal data, replacing the abnormal data with the average value to obtain second historical assay data; filtering the second historical assay data by adopting a particle filter to obtain third historical assay data; constructing and training an LSTM neural network model, and obtaining the LSTM neural network model after a set number of training times; and sequentially processing the multi-component ore content data to be measured through steps 1-4 to obtain a prediction result of the multi-component ore content. Compared with the prior art, the application can effectively identify and process abnormal points in data, improve the precision and robustness of a model, and improve the accuracy of predicting multi-component ore content.

The application discloses a kind of for mathematical formula identification and extraction method and system, method includes: obtaining input original text data;The original text data is preprocessed, and the text information after preprocessing is obtained;Formula identification extraction method is used to extract mathematical formula to the text information after preprocessing, and extraction result is obtained;Output extraction result.The method can accurately improve the accuracy and adaptability of mathematical formula extraction, and extraction efficiency is high, improve the efficiency of processing a large amount of text.