19 results about "Random field ising model" patented technology

The invention relates to the technical field of intelligent breeding, and discloses a beef cattle identification method based on multi-scale segmentation optimization and multi-modal data fusion, which comprises the following steps: acquiring beef cattle multi-modal image data, carrying out time-space synchronization and fusion to construct a fusion image, carrying out preprocessing of illumination invariant transformation and multi-scale pyramid construction, and carrying out multi-scale segmentation optimization and multi-modal data fusion on the fusion image; generating an enhanced image, inputting the enhanced image into a depth-guided attention segmentation network to extract double-branch foreground features, generating an initial segmentation probability graph, optimizing a segmentation mask in combination with a conditional random field model and a motion consistency constraint, finally extracting multi-dimensional features from the mask, inputting the multi-dimensional features into a multi-classification support vector machine to perform individual classification reasoning, and obtaining a final segmentation result. And obtaining a beef cattle individual identification result. Therefore, the foreground and background segmentation method applied to the intelligent breeding scene for beef cattle individual recognition is provided, the beef cattle individual recognition precision is improved, and meanwhile the management requirements of commercial breeding for high precision, high stability and multi-scene adaptability are met.

The invention provides a single photon and visible light image fusion method and system based on a multi-scale Markov random field model, and belongs to the technical field of multi-modal image fusion. In the long-distance target ranging and imaging process, a multi-sensor fusion method is used, the high resolution advantage of a visible light intensity image is utilized, the problems that a single-photon laser radar is small in point cloud density and low in image resolution are effectively solved, and the sensing capacity of the single-photon laser radar for scene target information is effectively enhanced; by using the fusion method based on the multi-scale Markov random field model, the structural consistency and the anti-interference capability are enhanced, global structural deviation or local overfitting under a single scale is effectively avoided, and the method is suitable for image fusion under multi-modal and complex scenes.

The invention belongs to the technical field of anti-seepage curtains of red-bed landfills, and discloses an optimal design method for a vertical anti-seepage curtain of an irregular landfill in a red-bed region, which adopts a drilling, geological radar and X-ray diffraction combined technology in the exploration and data acquisition stage to clearly distinguish hydrophilic expansive mudstone and high-permeability sandstone, and the optimal design method for the vertical anti-seepage curtain of the irregular landfill in the red-bed region is provided for the vertical anti-seepage curtain of the irregular landfill in the red-bed region. A space coupling model of the pile body and the red rock stratum is established by means of three-dimensional laser scanning, and the lithologic boundary of the dual seepage system is defined; in the model construction stage, parameter distribution is analyzed through a Markov random field model, regular updating is conducted in combination with a Kalman filtering algorithm, and dynamic changes such as sandstone fracture expansion caused by pile settlement can be adapted; a modified bentonite and nanoscale montmorillonite composite material is used in a sandstone area, construction is conducted through a high-pressure jet grouting technology, the permeability coefficient is set to be that the insertion depth extends to the complete bed rock by 2-2.5 m, and the problem of local leakage caused by the fact that the seepage-proofing design is not matched with the actual conditions of a red layer is solved.

This invention discloses an environmental anomaly broadcasting method and system based on semantic analysis and knowledge graph, belonging to the field of environmental anomaly detection technology. Key technical points include: acquiring the current inspection record text and performing semantic analysis to obtain text keywords; determining the nodes corresponding to the text keywords in a preset knowledge graph based on a conditional random field (CRF) model; training the CRF model according to a preset constraint matrix; the types of nodes in the knowledge graph include enterprises, emission outlets, processes, pollutants, and sensitive targets; obtaining anomaly information corresponding to the current inspection record text based on the association relationship between the nodes corresponding to the text keywords and nodes of each type in the knowledge graph; and broadcasting the anomaly information. This invention improves the accuracy of node positioning and generates more accurate anomaly information by constructing a constraint matrix to clarify the matching rules between text and nodes and training the CRF model accordingly.

The application relates to the field of artificial intelligence and discloses a target recognition method and system based on deep learning, which comprises the following steps: acquiring visible light and infrared images of the same scene, generating illumination invariance features through an adversarial training network; modeling the feature sequence by using a space-time joint network to generate an initial recognition result; performing space-time verification on the initial recognition results of continuous multiple frames by using a Markov random field model to generate a calibrated recognition result; and performing online model calibration on the space-time joint network based on the correction deviation between the initial recognition result and the calibrated recognition result. The application also provides a recognition system for executing the method. Through the synergistic mechanism of multi-modal feature decoupling, space-time joint modeling and verification, and online feedback calibration, the application effectively improves the all-weather adaptability, recognition accuracy and trajectory continuity of the system in complex scenes such as illumination changes and target occlusions.

This application relates to the field of relational network analysis in artificial intelligence, specifically to a user differentiation method, apparatus, device, and medium based on content and network features, comprising: acquiring a social network graph; acquiring node features of each node based on the social network graph; inputting the node features into a Markov random field model to obtain a first classification result of the node; extracting content features from the content information; inputting the content features into a trained content classification model to obtain a second classification result of the content features; and determining the user type of the node based on the first classification result and the second classification result. This application combines the different characteristics of content features and network features, utilizing more comprehensive user information to detect whether users on social network platforms are spam accounts, making it less likely for malicious users to bypass the detection.

The application provides a coal mine main fan frequency converter speed regulation method based on random wind speed field prediction. First, wind measuring points are arranged in underground stoping working faces, tunneling working faces and air shafts. According to various data monitored by the wind measuring points, information prediction is carried out, and a graph neural network is established to calculate air supply. According to the real-time air volume calculated by the graph neural network, the relationship between the control frequency of the main fan frequency converter and the real-time air volume is obtained, and the frequency converter control frequency is calculated. The beneficial effect is that the present application takes the random field as the theoretical basis, takes each key ventilation detection in the underground as the vertex of the random field, takes the ventilation detection result as the attribute of the vertex, establishes an underground ventilation control random field model, then establishes a field data prediction model to predict the future information of the wind field, thereby guiding the ventilation regulation of the main fan and effectively improving the response speed of the ventilation control.

The application belongs to the field of digital protection of cultural heritage, and discloses a three-dimensional modeling method for ancient buildings based on three-dimensional laser scanning, which comprises the following steps: extracting feature points of point cloud data sets and texture image data and combining with unmanned aerial vehicle flight logs for registration; performing image restoration and feature extraction on the texture image data and point cloud-image registration results through a convolutional neural network-probabilistic Markov random field (CNN-PMRF) model; performing geometric feature extraction on the point cloud data sets and fusing texture image feature vectors to obtain geometric-texture joint feature vectors, and optimizing a rough grid model generated based on the point cloud data sets; mapping the restored texture image data to the optimized grid model to obtain a preliminary three-dimensional grid model; processing preliminary model parameters through a gradient boosting decision tree (GBDT) model to obtain a correction value, and adjusting the preliminary three-dimensional grid model based on the correction value to obtain an optimal three-dimensional model. The application can improve the accuracy of three-dimensional modeling of ancient buildings.

The invention belongs to the technical field of preparation of rock samples, and particularly discloses a preparation method of a rock sample containing space correlation random joints based on 3D printing, and the preparation method comprises the following steps: manufacturing a sample outer mold and a joint steel insertion strip according to the specification of the rock sample; the method comprises the following steps: acquiring spatial distribution parameters of random joints in a rock mass through geological survey, and generating a spatial correlation random joint group by adopting a random field model according to the acquired spatial distribution parameters of the random joints; a three-dimensional cover plate model is established according to the generated space correlation random joint group, cover plate holes used for installing joint steel insertion strips are formed in the three-dimensional cover plate model, and then a 3D printer is used for printing to obtain a 3D printing cover plate; and loading the prepared cement mortar into the assembled sample outer mold, covering with a 3D printing cover plate, inserting the joint steel insertion strip coated with the release agent into a cover plate hole, pulling out the joint steel insertion strip after the cement mortar is cured, and demolding and curing the sample to obtain the rock-like sample.

The invention discloses an expressway base layer global moisture content detection method based on a three-dimensional ground penetrating radar, which comprises the following steps: constructing a three-dimensional multiphase random medium electromagnetic simulation model by considering aggregate grading and a pore structure; based on the model, simulating the propagation process of radar waves in a base layer under different moisture content conditions to obtain simulated radar reflection wave data; the A-scan waveform is converted into an A-scan waveform; extracting a reflected wave mean value, a standard deviation and a peak value coefficient, so as to establish a reflected wave intensity-dielectric constant-moisture content three-variable regression model; based on a line tracking theory, inversing an actually measured dielectric constant through actually measured reflected wave two-way travel time and horizon thickness; calculating actually measured moisture content data by utilizing the actually measured dielectric constant obtained by inversion; and constructing a complete random field model representing the spatial variability of the moisture content, and carrying out constraint and calibration on the random field model by taking the actually measured moisture content as a sample point through a Kriging interpolation method, so as to generate a base-layer global moisture content visual distribution diagram.

A large-size structural plane first-order and second-order fluctuation grading sampling measurement method belongs to the technical field of geotechnical engineering, and comprises the following steps: measuring the length of a structural plane along a shearing direction, determining a frequency demarcation point of first-order and second-order fluctuation components according to an empirical formula, and determining a sampling interval of the first-order fluctuation components, performing sparse measurement on the large-size structural surface; the method comprises the following steps: arranging a plurality of small-size measurement areas on a structural plane, obtaining a two-dimensional contour line or a three-dimensional structural plane morphology, extracting a second-order fluctuation component through frequency domain filtering, calculating a fluctuation angle statistical characteristic, evaluating a measurement error based on a confidence interval, and adaptively determining the measurement number of small-size structural planes; and establishing a random field model of the second-order fluctuating component, generating the second-order fluctuating component with the same size as the large-size structural plane, and superposing the second-order fluctuating component with the actually measured first-order fluctuating component to obtain a structural plane digital model. On the premise of guaranteeing the measurement precision, the number of measurement points and the measurement workload are reduced, and the applicability of large-size structural plane measurement engineering is improved.

The application discloses a kind of narrow river detection methods suitable for spaceborne wide swath interferometric radar altimeter image, comprising: reading into spaceborne wide swath interferometric radar altimeter image;It is enhanced river linear feature by Gaussian difference preprocessing to suppress background;Curvature structure perception detector is constructed, curvature response map is calculated based on Hessian matrix eigenvalue, and feature fusion enhancement is carried out in conjunction with multi-direction structure consistency score;The binary image obtained by adaptive threshold segmentation based on local statistics is used as region label by carrying out adaptive threshold segmentation to enhanced feature map, maximum value is extracted in the region corresponding to enhanced feature map, seed point set is formed, and adaptive region growth based on queue priority is executed, to generate initial river center line;Markov random field model is introduced, global structure optimization and topological connectivity correction are carried out using graph cut algorithm, to obtain optimized river center line;Direction and radiation characteristic constraint is applied again, morphological dilation is carried out, and finally complete river mask is generated.

The invention discloses a space-based infrared dim and small target detection and calculation integrated detection method based on a vector signal, and aims to overcome the bottleneck of low timeliness and large resource consumption of a traditional imaging-caching-preprocessing-detection link, skip an image caching and preprocessing link and directly take a DN value vector signal read by a detector row as input. Wherein two-dimensional or higher-dimensional features of a target are recovered from a one-dimensional vector signal through a network based on CNN and BERT mechanisms and by means of a Markov random field model; target and background clutter features are decoupled by using a network based on a self-supervised U-Net structure, and target features are selectively enhanced in combination with a multi-scale detection mechanism to complete detection; a network is lightened through a multi-teacher knowledge distillation architecture, and pruning and quantification are combined to obtain a lightweight detection model suitable for an on-satellite resource limited environment. According to the invention, integration of detection and calculation is realized, and timeliness and resource efficiency of space-based infrared weak and small target detection are significantly improved.

A method and system for 3D modeling of stochastic variation of a lithographic process. The lithographic process is subject to random stochastic phenomena, with the resulting stochastic randomness potentially becoming a major challenge. The stochastic phenomena are modeled using a stochastic model, such as a random field model, that models stochastic randomness. To extend the application of the stochastic model to predict 3D aspects and increase the accuracy of modeling, the stochastic randomness for each level of a plurality of levels discrete from one another in a resist thickness direction may be modeled and analyzed across the plurality of level to generate a 3-dimentional distribution of the stochastic randomness. In turn, indications of 3-dimentional distribution of the stochastic randomness may be used to modify one or both of the light exposure and resist parameters in order to reduce the effect of stochastic randomness on the lithographic process.

This application provides a network processing method, apparatus, and storage medium. The method includes: a network processing method characterized by comprising: determining the link nodes to be detected in the network to be detected according to the network type of the network to be detected; determining the target feature function and feature parameters of the target feature function corresponding to each link node to be detected from a target random field model; determining the quality influence parameters of each link node to be detected according to the target feature function and feature parameters of the target feature function; and determining the quality defect probability of each link node to be detected based on the quality influence parameters of each link node using a confidence propagation algorithm. This method can achieve full utilization and efficient fusion of probe data at all levels. By combining the random field model with the confidence propagation algorithm, the quality defect probability of each link node can be quantitatively determined, improving the accuracy of network quality judgment.

This application provides a slope uncertainty modeling method and related equipment based on micromotion analysis. The slope uncertainty modeling method includes: acquiring micromotion signals of the slope collected by a detector array; obtaining a shear wave velocity profile of the slope based on the micromotion signals; the shear wave velocity profile is used to reflect the number of strata, the shear wave velocity of each stratum, and the thickness; fitting the autocorrelation function of the strength parameters of each layer and the corresponding correlation length based on the shear wave velocity profile; the correlation length is the horizontal correlation length; and constructing a random field model of slope strength parameters based on the autocorrelation function and the correlation length to achieve slope uncertainty modeling. This method can accurately obtain the parameters required for constructing the random field, especially the horizontal correlation length, thus improving the accuracy of the final obtained slope parameter random field model.

The invention discloses a narrow river detection method suitable for a satellite-borne wide-cradling interference radar altimeter image. The method comprises the following steps: reading in the satellite-borne wide-cradling interference radar altimeter image; gaussian difference preprocessing is carried out to suppress background and enhance the linear features of the river channel; constructing a curvature structure sensing detector, calculating a curvature response diagram based on a Hessian matrix eigenvalue, and performing feature fusion enhancement in combination with a multi-direction structure consistency score; performing self-adaptive threshold segmentation based on local statistics on the enhanced feature map to obtain a second map as a region mark, extracting a maximum value in a corresponding region of the enhanced feature map, forming a seed point set, executing self-adaptive region growth based on queue priority, and generating an initial river channel center line; introducing a Markov random field model, and performing global structure optimization and topological connectivity correction by using a graph cut algorithm to obtain an optimized river channel center line; and then applying direction and radiation characteristic constraints, performing morphological expansion, and generating a final complete riverway mask.

The invention discloses a business personnel seat number identification method and device, electronic equipment and a program product, relates to the field of artificial intelligence, and is applied to the field of financial science and technology, and the method comprises the steps: carrying out the preprocessing of collection information of a transaction liquidation request when the transaction liquidation request is detected, and obtaining a preprocessed text; extracting a text semantic feature, a character space feature and a business logic feature from the preprocessed text, and performing feature fusion to obtain a fused feature vector; and inputting the fusion feature vector into a target recognition model, and outputting a business personnel seat number recognized from the collection information, the target recognition model being obtained by training a preset recognition model by using historical collection information, and the preset recognition model at least comprising: a bidirectional long short-term memory network, a converter encoder, and a conditional random field model. Through the method and the device, the problem that the seat number of the business personnel in the collection information is difficult to identify based on a traditional keyword matching method in related technologies is solved.

The application discloses a groundwater physical parameter simulation method based on a Markov random field and a KD tree, relates to the technical field of groundwater environmental parameter simulation, and comprises the following steps: 1, drilling data with spatial correlation is acquired; 2, a Markov random field graph structure is initialized, and an adjacency matrix and an edge weight matrix are filled in through a KD tree; 3, a Markov random field model is constructed, and spatial smoothness is ensured through independent and identically distributed sampling and a Markov energy term; and 4, the physical parameters and uncertainty of a target position are inferred using the Markov random field model. The groundwater physical parameter simulation method based on the Markov random field and the KD tree solves the problems of uncertainty of physical parameter distribution, spatial strong correlation, sparsity of drilling data and information subject to a physical equation in existing groundwater physical parameter inversion.