35results about How to "Improve analytical ability" patented technology

The invention relates to the technical field of government affair services, in particular to an NLP entity recognition method based on semantic extension, which comprises the following steps: collecting multi-source time sequence data and generating an entrepreneurship ecological data graph through a dynamic time warping algorithm; dynamic evolution of a term library is realized by using a generative adversarial network, and a term semantic association graph is constructed in combination with a graph convolutional network; a double-path attention mechanism is deployed in a Transform encoder, and global semantics and local attention guided by terms are fused through a gating loop unit; modeling fuzzy query analysis into a Markov decision process, and optimizing a semantic extension strategy by adopting a reinforcement learning agent; multi-granularity features are extracted through a feature pyramid network, and model parameters are optimized in combination with a bidirectional long-short-term memory network and an online learning feedback loop; and finally, calculating entity service association strength by using a graph attention network, and generating an interpretable report based on a Shapley value attribution algorithm. A closed-loop learning system is formed, and the business starting policy matching accuracy is improved.

The invention provides a collaborative development process data synchronization and notification method. According to the method, an automatic process is triggered by monitoring a test-submitted event message queue, development data is positioned based on module identification, key operation features (such as database change and task start and stop) are analyzed and extracted by using natural language, and feature extraction precision is improved by combining a dynamic keyword library and cross-modal fusion; the timeliness and reliability of risk reminding are ensured through template message generation and multi-channel delivery; a risk propagation range is further predicted by means of a dependency graph model, and a closed-loop early warning mechanism is formed. According to the method, the problem of delay caused by information lagging and insufficient collaboration in the development process is solved, the risk of manual operation is reduced, and the efficiency and controllability of project management are improved.

The invention relates to the technical field of historical building structure health monitoring, and discloses a historical building safety performance evaluation method and system. The method comprises the steps of obtaining a structure response signal through monitoring of a multi-position sensor, and generating a fusion feature map through a multi-scale feature sensing network. And performing three-dimensional reconstruction based on the atlas, adaptively dividing difference precision entity units according to feature density, and constructing the high-fidelity digital twinborn body. And driving the twin to perform time-history evolution under simulation of long-term environment excitation, and extracting damage evolution behavior fingerprints. And performing multi-round iterative comparison and reverse traceability analysis on the behavior fingerprint and a safety baseline containing material time-varying degradation and component interaction influence, identifying a failure mode and an evolution path, calculating a residual bearing capacity margin of a key component, and generating a structural life map in combination with a component interaction network. According to the method, high-fidelity modeling based on monitoring data and reverse intelligent diagnosis of dynamic damage evolution are realized.

The application relates to a self-adaptive self-powered flexible sensing device based on triboelectricity and a preparation method thereof, which comprises a flexible device, a charging module and a single-chip circuit board; the flexible device comprises, from top to bottom, a triboelectric layer, an insulating layer, a potential layer and a battery layer; the triboelectric layer is connected with the charging module, the charging module is connected with the battery layer, and the battery layer is connected with the single-chip circuit board. The system collects signals of multiple electrode ports through a single-chip microcomputer and transmits the signals to a mobile phone or a computer terminal through Bluetooth wireless transmission, so that the convenience and expansibility of data acquisition are improved, and real-time monitoring and remote interaction application can be constructed.

The present application relates to the technical field of data analysis, in particular to a data analysis processing method and system for processing a chromatographic system, comprising the following steps: collecting a chromatographic matrix, calculating curvature variation, constructing a segmented function, mapping to a manifold space, screening a drift retention trunk, calculating a difference and a variation rate, cutting a high variation area, constructing a three-dimensional tensor, performing sparse decomposition to obtain an independent peak component, locally fitting to extract a peak shape moment feature, reconstructing a peak shape according to a fitting weight to generate an identification identifier, in the present application, a manifold space is constructed based on curvature mutation point division, a signal trunk is identified using a gradient variation rate and drift noise is screened out, a high gradient section of a signal response surface is constructed in combination with a retention time difference and a variation rate, potential overlapping peak areas in a complex structure are effectively located, a local subsection is fitted by a least square method and a peak shape moment feature is extracted, weighted reconstruction is performed in combination with global information, and the refinement expression of peak shape information and the enhancement of recognition accuracy are realized.

The application belongs to but is not limited to the technical field of intelligent education, education data mining and artificial intelligence, and discloses a cognitive dynamic tracking method and system based on asymmetric hierarchical perception and ordinal constraint, which carries out structured mapping of a target level of a learning task, a knowledge type and a question concept set, constructs a multi-dimensional heterogeneous feature group containing sequence constraint education semantics and behavior characteristics, and fuses basic interaction features and education classification method features through a gating mechanism to generate a deep interaction feature sequence; and based on a directed cognitive level difference, an asymmetric hierarchical bias matrix is constructed, which is introduced into a multi-head self-attention mechanism to realize adaptive dynamic perception and updating of a learner's cognitive level; further, the knowledge state and the cognitive level are continuously evolved and tracked in the whole learning process, and an interpretable cognitive level evolution track, a phased transition point and a future answer prediction result are output, which can be used for ability diagnosis, personalized recommendation and learning path optimization.

The present application relates to the technical field of smart city, especially relates to a smart city bus stop board dynamic information accurate pushing method and system, wherein a query semantic representation structure is constructed by collecting query requests and scene state information, a joint understanding representation is obtained by modeling the scene state information multi-level semantic association, scene constraint multi-hop reasoning is executed in a bus knowledge association network, a candidate answer sequence is generated by filtering, an optimal answer is selected by calculating a scene adaptation score, pushing content is generated, interactive feedback is collected, feature fusion weights are adaptively adjusted to form a closed loop optimization, and accurate dynamic pushing of bus information and user experience improvement are realized.

The application discloses a cell traction force and microenvironment pH synchronous in-situ detection method based on Raman spectrum. Gold nanoparticles solution and thiol-modified polystyrene microsphere dispersion liquid are mixed to assemble gold nano clusters; 4-thiopyridine is modified to be a SERS probe; the SERS probe is mixed with a hydrogel prepolymer solution, the surface of the polymerized hydrogel is modified with fibronectin, and the living cells to be detected are inoculated; the substrate Raman spectrum is acquired and a heat map is drawn, the probe displacement is characterized according to the heat map change, and then the cell traction force size is characterized, and the cell microenvironment pH is characterized according to the characteristic peak intensity ratio in the Raman spectrum. The application realizes the essential fusion of the traction force microscope technology and the surface enhanced Raman scattering technology from the detection mechanism level, the detection dimension is expanded to the synchronous acquisition of the mechanical and biochemical information, so that the potential force-chemical coupling mechanism in the cell behavior process can be revealed.

The application relates to the technical field of medical image analysis, in particular to a classification method and system based on thyroid or breast nodules, which comprises the following steps: acquiring a continuous image frame sequence to locate a calcification region mapping space coordinate, associating adjacent frame homologous segments, extracting a nodule outer edge feature corresponding to a final frame calcification position, analyzing a gray peak value direction connectivity, extracting a texture aggregation boundary feature and overlapping volume curvature and echo distribution analysis to obtain a classification result. In the application, the spatial position and time variation of the calcification region in the continuous image frame are jointly analyzed to obtain a dynamic migration track corresponding to the nodule outer edge feature, a propagation path is constructed in combination with the direction distribution of the internal gray peak value, the boundary contact feature and the texture aggregation form are matched in a unified space, a multi-dimensional feature set with spatial continuity and direction integrity is formed, the analysis ability of the nodule volume, curvature and internal structure difference is improved, and the stability and distinguishability of the classification result are enhanced.

The invention discloses a biological entity multivariate association prediction system combining linear and nonlinear fusion matrix factorization, which relates to the technical field of biological entity multivariate association prediction and comprises a multivariate biological entity input module, a cross-modal feature extraction module, a dual-channel fusion matrix factorization module, a dynamic feature fusion device and a multivariate association prediction engine. According to the method, the limitation of a traditional biological entity association prediction method is broken through by fusing linear and nonlinear matrix decomposition technologies, and a dynamic feature fusion mechanism realizes optimal combination of cross-modal features through adaptive weight adjustment, so that the problem of insufficient flexibility of a traditional static fusion strategy is overcome; in addition, the system adopts a three-dimensional tensor modeling technology, a unified prediction framework of multiple types of associations such as gene-disease, drug-target, metabolite-pathway and the like is realized, heterogeneity characteristics of biological associations can be effectively maintained, and compared with the prior art, the analysis capability of a complex biological network is remarkably improved.

This invention relates to the field of current measurement technology and discloses a method and system for improving the accuracy of NV center current measurement in diamond. The method includes: applying an axial magnetic field outside the diamond sample; comparing the energy level difference between two characteristic states with the target spin; determining whether energy level anti-crossing exists based on the comparison result; determining the fluorescence intensity based on the energy level anti-crossing; determining a normalized fluorescence signal based on the initial fluorescence signal and the target fluorescence signal; determining the coupling relationship between the magnetic field and fluorescence intensity based on the fluorescence intensity, the normalized fluorescence signal, and the placement probability; applying a bias magnetic field opposite to the axial magnetic field based on the open-loop transfer function and the microwave frequency; modulating the measurement microwave frequency with a sine wave; and measuring the NV center current based on the Fourier coefficients, the adjusted signal-to-noise ratio, and the bias magnetic field. This invention improves the accuracy of current measurement.

This invention relates to a method for real-time online detection of wastewater indicators. Before detection, a low-concentration water body is injected into the detection chamber to perform micro-dose detection. By observing whether there is a nonlinear shift in the initial response behavior, it is determined whether there is residual response on the inner wall of the detection chamber. If so, a local delayed detection and pre-rinsing operation is performed to suppress interference responses caused by the chemical memory of the detection chamber. After completing the pre-response suppression, a slow-varying physical stimulus process is applied to the wastewater to be tested, stimulating the release of masked pollutants in the wastewater. The pollutant response signals are continuously tracked, and fluctuating behavior is identified. Based on the changing trend of the response curve, a stable response interval is identified as the sampling point for final detection. For cases with superimposed responses from multiple pollutants, the rise rate, fall rate, and tailing characteristics of the response profiles of each indicator are analyzed to identify the physical differences of multiple response signals and perform physical-level decoupling, improving the accuracy of identifying multiple pollutants.

The application provides a kind of urban water environment water quality synchronous detection device and detection method based on space-time spectrum strong association;Device includes excitation light source module, light splitting modulation module, micro-volume flow-through sample cell, fluorescence receiving module, transmission receiving module, scattering receiving module, physical and chemical parameter synchronous detection module, data acquisition control module and data processing module;The application constructs space-time spectrum joint data set based on synchronous data acquisition, realizes complex mixed response decoupling through space-time spectrum strong association constraint and physicochemical-optical coupling constraint, and realizes quantitative analysis of key biochemical characteristic elements of water body based on pure pixel response spectrum extraction and non-negative quantization.The application can reduce sample consumption, shorten response time, and realize rapid detection, accurate identification and dynamic monitoring of key physicochemical properties and optical behavior characteristics of water body, and has advantages such as easy cleaning and maintenance of sample cell, low risk of cross contamination, high synchronicity and strong interpretability of analysis results.

This invention discloses a PolSAR image classification method and related apparatus based on a complex convolutional Kolmogorov-Arnold network, belonging to the field of land cover classification technology. The method includes: acquiring a PolSAR image to be processed and performing preprocessing; inputting the preprocessed PolSAR image into a preset complex convolutional Kolmogorov-Arnold network to obtain a classification result; wherein the preset complex convolutional Kolmogorov-Arnold network includes complex KAN convolutional layers, multi-branch complex KAN convolutional blocks, and CV-PolyLoss. This invention has promising prospects for practical remote sensing application deployment.

A method for inverting physicochemical parameters of plant leaves based on spectral two-dimensional representation and multi-task deep learning belongs to the field of plant physiological detection technology. The method is as follows: Construction of a hyperspectral plant leaf dataset; preprocessing of the hyperspectral plant leaf dataset; reconstruction of the preprocessed one-dimensional spectral vector into a two-dimensional image; using the generated two-dimensional image as input, constructing a multi-task learning model and training and evaluating it; applying the trained model to a test set to obtain the inversion results of leaf physicochemical parameters. This invention solves the parameter inversion interference problem caused by the phenomenon of different substances with the same spectrum, achieves accurate modeling of local fine feature extraction and global contextual relationships, solves the inversion accuracy bottleneck caused by overlapping spectral responses between parameters, improves the model's ability to distinguish easily confused parameters such as chlorophyll and carotenoids, and ultimately achieves high-precision and robust synchronous inversion of plant leaf physicochemical parameters.

This application relates to the technical field of computer vision, and in particular to a method and system for identifying paint formulation components based on multi-angle and multimodal measurement. The method includes: acquiring multimodal measurement data of a paint sample under test from multiple angles, including image data, spectral data, and texture data; extracting and fusing features from the multimodal measurement data to obtain a formulation-level feature vector; determining the types of colorants contained in the paint sample and the presence confidence of each colorant based on the formulation-level feature vector and a pre-constructed colorant standard feature library, using an attention mechanism and a multi-label classifier; and for colorants whose presence confidence meets preset conditions, performing physically constrained regression prediction based on the formulation-level feature vector, the colorant standard feature library, and the colorant presence confidence to determine the content proportion and content prediction confidence of the colorants. This application improves the formulation identification effect and accuracy under multiple colorant conditions.

The invention relates to the field of dairy product foam moisture migration and stability correlation analysis, in particular to a dairy product foam moisture migration and stability correlation analysis method. The method comprises the following steps: firstly, based on an obtained dairy product foam sample, acquiring a relaxation signal, performing normalization processing to obtain a nuclear magnetic resonance signal sequence, introducing a signal smooth extension method to obtain a nuclear magnetic energy density function, and further constructing a composite nonlinear enhancement function; then, based on the composite nonlinear enhancement function, constructing a migration gradient response function; obtaining a migration probability based on the migration gradient response function; calculating a moisture migration entropy weight index based on the migration probability; and finally, based on the moisture migration entropy weight index, obtaining a foam structure stability estimated value, and obtaining an analysis result. The technical problems of non-destructive, quantitative and dynamic identification of internal moisture migration behaviors and establishment of a mathematical mapping relation with physical interpretation force for moisture migration characteristics and foam structure stability are solved.

The invention provides a medium and long term electric quantity prediction method and system based on an improved support vector machine. The method comprises the following steps: inputting time sequence data representing historical power consumption into a state projector for data dimension reduction to obtain a feature state vector representing a power consumption core mode; inputting the feature state vector into a state predictor to obtain an initial electric quantity prediction sequence of a plurality of time steps in the future; performing multi-scale decomposition on the feature state vector to obtain feature subsequences containing different frequency components; constructing a support vector machine prediction model based on the feature subsequences, and optimizing key parameters of the support vector machine prediction model by adopting a differential evolution algorithm to obtain an optimized hybrid support vector machine model; and based on the hybrid support vector machine model, correcting the initial electric quantity prediction sequence to output a target electric quantity prediction sequence of a plurality of time steps in the future, and the method can predict the energy demand according to different conditions for an analysis result with complex energy demand characteristics.

The application provides a bearing fault diagnosis method fusing vibration and acoustic emission multi-source information, and the method is used for fault diagnosis classification according to bearing vibration signals and acoustic emission signals under complex working conditions. By constructing a symmetrical CNN-GRU subnetwork structure, deep features of two kinds of modes are independently mined, and batch normalization and Dropout regularization strategies are combined to enhance the stability and robustness of training. On this basis, a spatial and channel double attention mechanism is introduced to dynamically weight and screen feature maps, eliminate redundant background noise, and effectively fuse multi-source information through a deep convolutional layer. The method effectively overcomes the defect that a single signal fails in a specific stage due to low signal-to-noise ratio, and significantly improves the reliability of the diagnosis system.

The application provides a low-distortion high-pixel optical system and an applied camera module, which are mainly composed of eight lenses, wherein the first lens has a convex object plane, a concave image plane and negative optical power; the second lens has a convex object plane, a concave image plane and negative optical power; the third lens has a concave object plane, a convex image plane and positive optical power; the fourth lens has a convex object plane and positive optical power; the fifth lens has a convex object plane, a convex image plane and positive optical power; the sixth lens has a concave object plane, a concave image plane and negative optical power; the seventh lens has a convex image plane and positive optical power; and the eighth lens has a convex object plane, a concave image plane and negative optical power. The application has reasonable lens number, simple structure, optimized lens aberration, improved resolution performance, wide angle, small distortion and high resolution, and is applied to a video conference equipment in a video transmission technology, so that image noise can be reduced, good imaging quality can be realized, and post-processing algorithm is facilitated.

The present application relates to the technical field of heat digital twinborn analysis, in particular to a three-dimensional electro-catalytic oxidation device temperature field collaborative regulation method based on digital twinborn. The specific implementation process includes: extracting current ripple features and fluid damping fluctuation data in the operation process of the three-dimensional electro-catalytic oxidation device, and constructing an energy distribution domain in the digital twinborn body; tracking the energy accumulation center in the energy distribution domain and correcting the heat conduction; carrying out evolution simulation to predict the spatial migration and temperature change trend; superimposing based on the prediction result to generate control waveform flow for device regulation; constructing an energy interaction link to collaboratively regulate the global temperature field. The present application combines multi-source data fusion, full-field reconstruction and prediction-driven control strategy, improves the limitations of relying on limited measurement points and lag feedback regulation, realizes high-precision perception, prediction and collaborative optimization control of the three-dimensional electro-catalytic oxidation device temperature field, and improves the stability of the device operation.

This invention relates to the field of seismic wave analysis technology, specifically a method for distinguishing and correlating ground motion parameters of the upper and lower reservoirs of a large pumped storage power station. The method includes the following steps: acquiring three-dimensional wave velocity and coordinate data of the upper and lower reservoirs through remote sensing mapping and drilling tests; modeling and simulating the wave field to output acceleration response; extracting phase abrupt change points, calculating density functions to generate a comparison table; statistically analyzing the offset and density distribution of abrupt change points; calculating normalized offset rates to form a discriminant dataset; extracting peak acceleration and response spectrum parameters to construct a feature set; training a random forest regression model to establish a mapping relationship; and introducing topographic propagation coefficients for calibration to generate a correlation table. In this invention, by jointly modeling topography and wave velocity to reconstruct the influence of structural differences on seismic wave paths, segmenting acceleration sequences to analyze abrupt change characteristics, and extracting peak values ​​and spectral points to quantify response differences, the method improves the analytical capability and matching degree of ground motion characteristics.

The invention relates to the technical field of optical detection, and discloses a method for detecting the collagen content of pigskin. The method establishes an initial optical profile of a pigskin sample before contacting a detection medium. And generating a special detection path aiming at the sample according to the contour, and specifying a moving track and a staying node sampled by the probe. Injecting a medium at a path starting point, and starting a synchronization mechanism to synchronize probe movement with a medium diffusion front edge; when the probe arrives at the termination node, the spectrum sequence of each node on the path is completely recorded, and the sequence comprises the dynamic characteristic absorption information of the collagen. And carrying out cross-node correlation fusion on the spectrum sequence to generate a fusion spectrum characteristic spectrum of the sample. And comparing the map with a pre-stored feature-content mapping model, and outputting a collagen content value. According to the invention, adaptive sampling and dynamic process tracking are realized, and the accuracy and reliability of detection are improved.

The invention discloses a semantic analysis method for a dynamic compensation neural network. The semantic analysis method comprises the steps of 1, input preprocessing and basic feature extraction, 2, initial semantic analysis and error evaluation, 3, execution of error compensation and 4, semantic fusion and output after compensation. According to complexity and domain characteristics of input semantics, compensation nodes can be adaptively activated, connection weights can be adjusted, multi-scene semantic requirements can be flexibly adapted, a real-time error feedback mechanism effectively inhibits underlying feature extraction deviation and analysis errors of a rare semantic mode, robustness is enhanced, a lightweight basic network is combined with an on-demand compensation strategy, and multi-scene semantic requirements can be met. The reasoning efficiency is optimized, the response requirement of a real-time interaction scene is met, high-precision, high-efficiency and high-adaptability semantic analysis capacity is finally achieved, and the method can be widely applied to natural language processing scenes such as intelligent question answering and dialogue systems.

The application relates to the technical field of file analysis, and discloses a user package file analysis method, system, device and medium based on load balancing, which comprises the following steps: carrying out file splitting on a user package file to obtain a user file list; selecting a target server cluster in a preset backend server cluster according to the user file list; calculating the real-time computing resources of each backend server in the target server cluster, and constructing a file allocation strategy according to the real-time computing resources; carrying out file analysis according to the file allocation strategy, calculating the analysis real-time computing resources after file analysis; and iteratively optimizing the file allocation strategy according to the analysis real-time computing resources until each user file is analyzed to obtain the analysis result of the user package file. According to the application, the user files can be reasonably allocated for file analysis according to the real-time load conditions of the backend servers, so that the computing resources of the backend server cluster can be reasonably utilized, and the stability and accuracy of user file analysis can be effectively improved.

The invention relates to the technical field of computer network security, provides a software backdoor detection method and device based on API (Application Program Interface) call sequence feature mining, and solves the technical problems of low detection accuracy and insufficient generalization ability of a backdoor program with high concealment and multi-modal features. The method comprises the steps of obtaining a target API calling sequence and network flow data in a target software running process, and obtaining an arrival time interval sequence; based on a preset API semantic mapping table, converting the target API calling sequence into a feature vector sequence, and converting the arrival time interval sequence into a discrete time interval sequence; constructing a heterogeneous behavior graph by mapping each feature vector in the feature vector sequence into a behavior node; inputting a pre-trained graph isomorphic network model to obtain a node embedding vector; and the node embedding vectors are combined to determine that the backdoor behavior exists in the target software, so that accurate detection of the software backdoor behavior with strong concealment and high camouflage degree is realized.

The present application relates to a kind of virtual image interaction method and system based on multimodal emotion adaptive network.Method includes: S1, obtains the multimodal input data of user in virtual image interaction process, multimodal input data includes voice data, facial image data and text data;S2, multimodal input data is extracted and fused with emotional characteristics, obtains emotional granularity joint representation matrix;S3, based on emotional granularity joint representation matrix, decoding obtains mixed emotional information representing the current emotional state of user, and according to preset ideal emotional label, calculate adaptive loss function value;S4, with adaptive loss function value as feedback signal, for the online parameter fine-tuning of emotional feedback generation network used to drive virtual image.The method can fuse the multimodal information of user to realize accurate emotional understanding, dynamically optimize the feedback behavior of virtual image, improve the emotional accuracy and response natural degree in interactive process.

The application relates to the technical field of optical detection, and discloses a pigskin collagen content detection method. Before contacting a detection medium, the initial optical profile of a pigskin sample is established. According to the profile, a special detection path for the sample is generated, and the moving track and the stay node of the probe sampling are specified. The medium is injected at the starting point of the path, and a synchronization mechanism is started to synchronize the probe movement with the medium diffusion front. When the probe reaches the terminal node, the spectral sequence of each node on the path is recorded completely, and the sequence contains the dynamic characteristic absorption information of collagen. The spectral sequence is subjected to cross-node correlation fusion to generate the fusion spectral feature map of the sample. The map is compared with a pre-stored feature-content mapping model, and the collagen content value is output. The application realizes adaptive sampling and dynamic process tracking, and improves the accuracy and reliability of detection.