24results 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.

The invention discloses a blood oxygen signal denoising method and system based on adaptive filtering, and the method comprises the steps: synchronously collecting an original blood oxygen signal and a three-axis acceleration signal through a sensor module, and obtaining an original blood oxygen signal sequence and a three-axis acceleration signal sequence which are aligned in time; the three-axis acceleration signals are fused into one-dimensional motion artifact reference noise signals used for representing the limb motion state of the user; the method comprises the following steps: dynamically calculating an adaptive step length factor sequence through a nonlinear mapping function based on multilevel energy analysis of a motion artifact reference noise signal; constructing a time-varying step size adaptive filter, and calculating noise components in the original blood oxygen signal by using the motion artifact reference noise signal and an adaptive step size factor; and subtracting the calculation noise signal from the original blood oxygen signal, and reconstructing to obtain a pure blood oxygen signal without motion artifacts. According to the method, accurate calculation and efficient suppression of motion artifacts can be achieved, and the purification quality and processing efficiency of blood oxygen signals are remarkably improved.

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.

The invention relates to a dynamic environment vision SLAM (Simultaneous Localization and Mapping) method based on double-source dynamic mask tracking. The method comprises the following steps: inputting a color image into a lightweight instance segmentation model for reasoning, generating a lagged semantic dynamic mask of a prior dynamic object, and asynchronously transmitting the lagged semantic dynamic mask to a front-end tracking thread; in a front-end thread, key points are detected based on a lagged semantic dynamic mask, optical flow tracking is carried out to a current frame, an overlook depth histogram is generated through the current frame, and a semantic dynamic mask of the current frame is obtained through screening and reconstruction. The geometric dynamic mask is calculated after the region is eliminated, and after the geometric dynamic mask which is not detected in the previous frame is supplemented, the geometric dynamic mask and the semantic dynamic mask are combined and collected to obtain a final dynamic mask. And filtering dynamic features by using the final dynamic mask, completing pose estimation and back-end optimization based on static features, updating a TSDF map by only using a static part, removing a changed foreground structure during revisit, and outputting a synchronous positioning and mapping result. By adopting the method, the positioning precision and the mapping quality of the visual SLAM system of the small unmanned platform can be improved.

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.

This application provides a method for extracting voiceprint features from speech segments with specific content. The method includes: obtaining an acoustic spectrum feature segment through preprocessing; constructing a time-delay neural network module; constructing a residual time-delay neural network module based on the time-delay neural network module, a weighted activation mechanism, and a residual structure; constructing a residual attention time-delay neural network module based on the time-delay neural network module, the residual time-delay neural network module, and an attention pooling mechanism; and inputting the acoustic spectrum feature segment into the residual attention time-delay neural network module to obtain the voiceprint features of the speech segment with specific content. The voiceprint feature extraction method provided here extracts deep-level information from features at multiple scales and, combined with residual networks, weighted activation, and attention pooling mechanisms, can effectively extract voiceprint features from speech segments with specific content.

The application relates to the field of engineering cost, and discloses an engineering cost data management method and system, which comprises the following steps: acquiring real-time collected cost data; if the cost data exceeds the monitoring threshold range of the corresponding project type, marking the cost data as a potential abnormal point and obtaining an abnormal data list; judging whether the abnormal point is related to the project type or the construction scale size based on the abnormal data list, regional market fluctuation data and material supply change characteristics, and obtaining an abnormal reason classification result; constructing a warning rule customization mechanism for regional market fluctuation to generate and verify a warning rule set suitable for the regional characteristics; acquiring new real-time cost data; and if the new real-time cost data triggers the conditions in the warning rule set, generating a corresponding warning signal. The method has the following effects: effectively identifying and analyzing cost data abnormalities.

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.

The invention relates to the technical field of water, wind and light power prediction, and discloses a wind power plant power limit data identification and correction method and device, and the method comprises the steps: employing a CNN-LSTM model to learn normal wind speed-power curve features, and through the combination of one-dimensional convolution and a pooling layer, effectively capturing the correlation features of wind speed and power, and carrying out the recognition and correction of the power limit data of a wind power plant. The identification accuracy and stability of the abnormal section of the wind power plant are improved; by adopting multi-dimensional wind regime feature analysis and similarity calculation, the complex wind power plant operation state can be effectively identified and processed, the limitation of only depending on a single feature in the prior art is overcome, and the comprehensiveness and accuracy of wind power plant abnormal data identification are improved; the weight of each similar section is determined based on the similarity of the fan states, and the reconstruction power value of the power limiting section is obtained through a weighted summation mode, so that the reliability and the accuracy of the operation data of the wind power plant are improved.

This application relates to the field of mass spectrometry analysis and detection technology, and in particular to a detection method for enhancing ion recognition, comprising the following steps: Step 1, placing an ultrasonic standing wave device within an ion source, the ultrasonic standing wave device including at least two oppositely arranged acoustic transducer units for emitting sound waves of the same frequency and opposite propagation directions to superimpose and form an ultrasonic standing wave field, and also including a triaxial adjustment mechanism; Step 2, the two acoustic transducer units extending into the ion source cavity and located on both sides of the nozzle, adjusting the position of the acoustic transducer units using the triaxial adjustment mechanism so that the acoustic transducer units are close to the rear of the nozzle, and the center height of the acoustic transducer units is lower than the tip of the nozzle; Step 3, adjusting the spacing between the two acoustic transducer units; Step 4, aligning the standing wave node axially with the entrance of the mass spectrometer detector. This method can enhance the focusing of sample ions, thereby improving the ion recognition effect of the mass spectrometer detector, without changing the internal structure of the ion source.

This invention provides a method and system for identifying interference sources in the auxiliary machinery loads of underwater platforms. Starting from small-sample electromagnetic signals monitored by the electromagnetic environment, it identifies electromagnetic interference signals in the underwater platform's power grid and autonomously analyzes the loads causing the interference, thus achieving effective analysis and identification of electromagnetic interference sources. Based on the characteristics of the auxiliary machinery loads in the underwater platform's power grid, this invention uses an equivalent physical model to simulate the causes of electromagnetic environment deterioration. Simultaneously, it establishes an intelligent electromagnetic interference signal identification model based on a BP neural network algorithm, achieving accurate identification of electromagnetic interference sources caused by changes in the underwater platform's power grid load, demonstrating strong engineering practical value. This invention reduces the efficiency of electromagnetic interference troubleshooting from "monthly" to "weekly" or "daily," significantly improving the efficiency of interference troubleshooting for underwater platform auxiliary machinery loads and reducing maintenance costs.

The invention relates to the technical field of drawing size intelligent identification, in particular to a software system and method for drawing size intelligent identification and proportional scale adjustment, and the software system comprises a file import module which is used for supporting various drawing files in common formats; and the image preprocessing and size recognition module is used for carrying out image preprocessing operation on the imported drawing image, automatically recognizing size lines, arrows and text labeling areas on the drawing by adopting an advanced mode recognition algorithm or a machine learning model, and recognizing size values through an OCR (Optical Character Recognition) technology. The method has the remarkable advantages of being high in automation degree, easy and convenient to operate, wide in application range and the like while the pain points in the prior art are effectively solved, great convenience can be brought to engineering designers, and the working efficiency of the engineering designers is remarkably improved.

This invention relates to the field of image recognition technology, and more particularly to a method and system for intelligent identification of coastal mangroves based on UAV remote sensing. The method includes: acquiring multi-band spectral data using remote sensing equipment, generating a standardized spectral feature dataset, calculating multi-band differences between adjacent pixels, and constructing a difference matrix to extract quantification indicators; marking potential transition points using a threshold segmentation algorithm, and performing temporal fusion with multi-temporal data to adjust dynamic offsets to obtain corrected transition features; grouping the corrected transition features using a clustering algorithm, determining critical position coordinates, and generating a refined boundary map using geographic information system data; and using interpolation methods to repair missing contour segments, ensuring contour continuity and obtaining the final mangrove boundary location result. This invention solves the problem of insufficient accuracy of traditional mangrove boundary extraction methods in dynamic environments and achieves high-precision intelligent identification of mangrove boundaries based on UAV remote sensing.

The application relates to the technical field of safety monitoring of water conservancy projects, and discloses a seepage intelligent monitoring system for water conservancy projects. The system comprises a parameter acquisition unit and a geological detection unit, which synchronously collect hydrological working condition data and geological radar scanning data. A feature generation unit fuses the two types of data to form an optimized multi-dimensional seepage feature set. A model construction unit uses the set to train an evaluation model and extracts a health state index. A real-time processing unit receives real-time operation parameters of the engineering structure, combines the evaluation model, and dynamically calculates a seepage health state index that is adaptive to the working condition. An early warning execution unit judges and triggers early warning according to the dynamic index and a preset risk mapping relationship. The application fuses multi-source heterogeneous data and realizes adaptive adjustment of the evaluation standard to the operation working condition, thereby improving the comprehensiveness of seepage risk evaluation and the accuracy of early warning.

This invention relates to the field of computer graphics technology, and more particularly to a method, system, device, and medium for verifying 3D models. The 3D model verification method includes the following steps: Step S1: Receive the user's detection command in real time and obtain the 3D model to be verified; Step S2: Analyze the 3D model to be verified and obtain geometric feature information and material feature information; Step S3: Match the geometric feature information and material feature information with the geometric feature information and material feature information of existing 3D models in the system's database one by one to determine whether they are consistent. If they are consistent, the first operation is performed; if they are inconsistent, the second operation is performed. This invention is aimed at 3D models with materials in the design industry, and achieves rapid storage and management of 3D models through a precise and effective verification method.

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.

This invention discloses a method and apparatus for generating urban DSMs using single-pass SAR for four-satellite formations, belonging to the field of radar measurement technology. The method includes: data preprocessing, performing precise registration on the complex SAR image of the four-satellite formation and removing terrain phase; homogeneous pixel extraction, determining a set of homogeneous pixels for each pixel as an adaptive multi-view window based on amplitude information; structural phase compensation, estimating and compensating for the non-stationary structural phase of strong targets; target height calculation, constructing a covariance matrix using the compensated interferogram and calculating pixel height using spectral estimation; outlier removal based on energy entropy dispersion index, filtering and removing outlier pixels; and geocoding, combining target height and orbital information to solve for latitude and longitude, generating the final DSM. This invention achieves high-precision, high-resolution urban DSM reconstruction using only a single-pass SAR data, effectively avoiding temporal decorrelation problems and significantly improving surveying efficiency and product quality.

The invention discloses an out-of-service power battery sorting method and device based on multi-modal information fusion, and relates to the technical field of battery recycling. Multi-modal input is formed based on one-dimensional structure features, a two-dimensional GASF image and a two-dimensional GADF image, and a multi-modal sorting model is constructed to realize modal deep feature extraction and interactive enhancement processing for classification prediction. Wherein the one-dimensional structure feature is extracted based on a partial discharge curve, full-coverage type charging and discharging are avoided, the time consumption of a charging and discharging experiment is shortened, and the data volume of model processing is reduced; the two-dimensional GASF image and the two-dimensional GADF image are obtained based on medium and high frequency band EIS data conversion, and the processing efficiency is considered under the condition that the integrity of key information is ensured. Compared with an existing sorting scheme which only depends on a single mode or is only based on capacity, the internal and external states of the batteries can be comprehensively reflected, and the consistency and accuracy of battery sorting are improved.

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.