1308 results about "Prediction probability" patented technology

Provided are a method and an apparatus for constructing a road congestion prediction model, a device, a medium, and a product. A road traffic network is defined as a directed weighted graph. Historical dynamic traffic features of each road segment in the road traffic network are obtained as sample data, including recent dynamic traffic features and periodic dynamic traffic features. The sample data is input into a mixture of adaptive graph learners (MAGL) model for learning, and a probability prediction vector is output. The sample data is input into a trend expert model, and a trend distribution vector of a predicted probability of future traffic conditions is output. The periodic dynamic traffic features are fused to determine a periodicity prediction vector. An aggregated logit vector is obtained. An objective function is determined based on the aggregated logit vector. Congestion prediction training is performed to obtain a road congestion prediction model.

The invention relates to the technical field of unit detection, in particular to a unit magnetic variable on-line monitoring method and system, and the method comprises the steps: carrying out the multi-source data collection through a sensor, carrying out the correction of the multi-source data through a multi-dimensional calibration mechanism, synchronizing a timestamp through a dual-synchronization system, and carrying out the frequency-band-divided conditioning and standardization processing; environmental noise in the standardized multi-source data is eliminated through an intelligent algorithm, feature vectors are extracted, and purified feature vectors are obtained; screening effective abnormal features through an isolation forest algorithm; and inputting the effective abnormal features into an LSTM prediction model to obtain a corrected prediction value, substituting the corrected prediction value into a logistic regression formula to obtain a fault prediction probability of fault occurrence, calculating a health degree score, and performing graded early warning according to the health degree score. According to the scheme, through multi-dimensional calibration, working condition adaptive feature extraction and graded early warning, the unit monitoring data precision, the fault feature recognition accuracy and the operation and maintenance decision efficiency are improved.

The invention relates to the technical field of energy consumption prediction, in particular to a building envelope energy-saving effect prediction method, which specifically comprises the following steps: deploying a sensor in a building maintenance structure to collect related data, and constructing a data set; marking the collected data to form a training set; a physical constraint decoupling normalization method is adopted for the data in the training set to generate features after decoupling normalization; a weighted feature vector is generated by adopting an attention mechanism guided by physical prior; constructing an energy-saving effect prediction network, and inputting the weighted feature vectors into the network for prediction; optimizing the network to obtain a trained network; newly-collected data is processed and then input into the trained network, the final prediction probability of each energy-saving grade is output, and the grade with the maximum probability is taken as a prediction result. According to the method, the collected data is preprocessed and then input into the network, so that the defects of inaccurate data processing, unreasonable feature selection and the like can be overcome, and the accuracy of a prediction result is improved.

The invention discloses a prostate cancer three-classification risk layering method based on an integrated learning model, and relates to the technical field of data processing and analysis. The method comprises the following steps: collecting clinical information and pathological data of a patient with increased PSA, and dividing the data into a training set and a test set; the training set is preprocessed, and prediction features are screened through LASSO regression; constructing a plurality of machine learning base models based on the features, and training and optimizing through cross validation; soft voting is constructed through an integration strategy, and an integration model is stacked; setting double thresholds according to the integrated model prediction probability, and establishing a layering rule; combining an integrated model and rules to form a three-classification model, and judging low, high and medium risks according to probabilities; and finally verifying the model diagnosis performance in the test set. According to the method, through cross-modal feature integration and ensemble learning, the method is PSAlt; accurate risk stratification is provided for 30 ng / mL people, biopsy decision-making efficiency is optimized, and excessive puncture and missed diagnosis risks are reduced.

The invention provides an intelligent machine room multi-dimensional dynamic monitoring management method and system based on digital twinning, and relates to the technical field of machine room monitoring, and the method comprises the steps: collecting a three-dimensional point cloud set and an RGB image set of a machine room space, and building a space mapping table; mapping the access control event data to a corresponding position in a digital twin space, generating an access control event space positioning set, identifying access control abnormal events, and constructing an access control abnormal event space positioning set; performing abnormal behavior recognition, and constructing an abnormal event graph structure; reconstructing and visualizing an abnormal track based on the abnormal event graph structure; in combination with the access control abnormal event space positioning set, the abnormal behavior category prediction probability distribution and the track risk level, executing a grading early warning mechanism, and generating an access control abnormal event log; according to the invention, intelligent positioning, track visualization and risk grading early warning of access control abnormal events in the intelligent machine room are realized, and the method is suitable for digital operation and maintenance management systems such as the intelligent machine room.

The invention relates to the technical field of digital image security and authentic identification, and provides an image tampering detection method and system based on mixed features and RGB features, and the method comprises the steps: obtaining a to-be-detected input image, and carrying out the preprocessing of the to-be-detected input image; respectively extracting a Haar wavelet high-frequency component, a discrete cosine transform frequency domain feature and a Bayer convolution noise feature, and carrying out matrix level fusion to obtain a mixed feature; extracting RGB (Red, Green and Blue) features for the preprocessed input image; the mixed features are connected through cross-layer residual errors, and mixed feature learning features are obtained; and integrating the mixed feature learning features and the fused RGB features by using a cross-modal feature interaction architecture to obtain a prediction probability graph. Multi-modal features are fused, high-frequency response is enhanced, and the accuracy of image tampering detection is improved by adopting a dynamic fusion mechanism. The technical problems that an existing tampering detection method is insufficient in feature characterization capacity in a complex scene, low in tampering trace detection sensitivity and the like are solved.

The invention discloses a systemic lupus erythematosus assessment method and device based on multi-modal medical map and medical knowledge fusion, terminal equipment and a medium, and relates to the technical field of medical artificial intelligence. The method comprises the following steps: acquiring multi-dimensional medical indexes of a patient, and constructing a multi-modal medical knowledge graph of the patient associated with the indexes; pre-training the self-supervised graph neural network to obtain a patient and medical index embedding vector; grouping and constructing a feature subset combination according to medical knowledge, and training and selecting an optimal sub-model; dynamically weighting and fusing the prediction probabilities of the sub-models to obtain a comprehensive prediction probability; and screening the key indexes based on the embedded vector and quantifying the activity evaluation contribution degree of the key indexes to obtain an activity comprehensive score. The systemic lupus erythematosus evaluation method improves the accuracy and robustness of preliminary evaluation of systemic lupus erythematosus, solves the problems of data missing and sample imbalance, and is explainable in output evaluation, adaptive to primary medical treatment and capable of supporting hierarchical diagnosis and treatment.

The invention discloses a flood disaster risk intelligent early warning method fusing multi-source information, and relates to the technical field of flood disaster prediction.The method comprises the steps that multi-source data of meteorology, hydrology, terrain, remote sensing, ground monitoring terminals and the like are obtained, and a unified data set is constructed through time synchronization, space resampling, abnormal value elimination and missing data filling; constructing multi-dimensional spatio-temporal features based on factors such as rainfall process, topographic features, surface coverage, drainage capacity, river network structure and soil humidity; inputting the spatial-temporal characteristics into a multi-source information fusion model, and predicting a flood occurrence probability in a future time period; flood risk indexes are generated according to the prediction probability, real-time monitoring and historical samples, and regional risks are divided into different grades; early warning information is automatically generated and issued according to the risk level; and the early warning result is fed back and corrected through field monitoring and patrol data, so that self-adaptive updating of early warning parameters and a model structure is realized.

The invention provides a spatio-temporal data fusion-based power distribution network load prediction method and system, and relates to the technical field of power distribution network load prediction, and the method comprises the steps: collecting related data of a power distribution network, carrying out the wavelet transform decomposition of historical load data, obtaining a load feature matrix, constructing a hierarchical graph convolution network based on topological structure data, and extracting topological correlation features; and generating a spatial-temporal feature tensor through tensor decomposition fusion, training a depth probability prediction model adopting a variational auto-encoder structure, and adjusting prediction probability distribution in combination with environmental data. According to the method, the prediction precision is improved, a complex space-time dependency relationship can be captured, and reliable uncertainty quantization is provided.

The invention belongs to the field of bioinformatics, and relates to an improved integrated deep learning cell communication ligand-receptor interaction prediction method. The method comprises the following steps: firstly, carrying out extraction and dimensionality reduction on biological sequence features of a ligand and a receptor, and constructing multi-modal feature input; secondly, constructing an improved deep neural network branch, introducing a batch normalization layer and a Leaky ReLU activation function, solving the problems of gradient disappearance and neuronal necrosis, and improving regularization strength to prevent overfitting; meanwhile, an enhanced heterogeneous graph auto-encoder branch is constructed, the graph embedding dimension is remarkably expanded to improve the feature capacity, and full convergence of the model is ensured by increasing training rounds; thirdly, fusing the improved deep network with the prediction probability of a heterogeneous graph auto-encoder by adopting a weighted integration strategy; and finally, outputting a potential interaction relationship based on the fusion probability. By optimizing the architecture and the strategy, the prediction accuracy and robustness are remarkably improved, and a reliable tool is provided for analyzing a complex cell communication network.

The invention belongs to the technical field of artificial intelligence, and particularly relates to a service-oriented robot-oriented multi-modal emotion recognition method and system, and the method comprises the steps: collecting audio and video stream data of emotion changes of a user, and separating visual and voice data; extracting visual and voice emotion features through a pre-training model, and calculating prediction probability distribution of each mode; constructing a bimodal confidence quantitative model based on the distribution to obtain each modal confidence; and fusing the features by adopting a sectional type dynamic weight distribution strategy so as to identify the emotional state of the user. Visual and voice modes are fused, feature alignment is realized in combination with dynamic time warping, spatial optimization performance is shared and expressed through a confidence model, a dynamic weight strategy and a cross-modal time sequence cooperation module, and the method has high recognition accuracy, high robustness and real-time processing capacity in a complex environment and is suitable for various service scenes.

The invention discloses a new energy multi-mode fault diagnosis method based on dynamic graph convolution and transfer learning. According to the method, multiple types of sensors are deployed to collect operation, environment and historical data of new energy equipment, and a multi-modal data set is generated through preprocessing; the method comprises the following steps: defining an equipment component as a graph node, constructing a directed graph containing a time-varying edge weight by utilizing dynamic time warping and an attention mechanism, and updating a graph structure on line; a dual-branch dynamic graph convolutional network is adopted to extract multi-modal data features, an adversarial transfer learning algorithm and a meta-learning algorithm are combined, distribution of a source domain and a target domain is aligned, and a special diagnosis model is generated by using small samples. And fault classification is carried out, a fault component is positioned in combination with a graph node attention weight, and a fault evolution index is constructed by fusing a fault prediction probability and an attention weight change rate. The method effectively fuses multi-modal data, adapts to complex working conditions, improves small sample diagnosis precision, realizes fault accurate positioning and evolution prediction, and has important significance for guaranteeing stable operation of new energy equipment.

The invention provides a geological environment monitoring method and system, and the method comprises the steps: constructing an air-space-earth-depth four-dimensional cooperative monitoring network, collecting multi-modal geological environment data, carrying out the preprocessing of the data, and obtaining multi-modal feature data and abnormal data points after dynamic load balance distribution of calculation resources; fusing the data to generate a comprehensive geological environment feature map containing space-time correlation features, abnormal hot spot distribution and a geologic body three-dimensional reconstruction model; and based on the map, performing geological risk prediction by using a geological risk prediction model to obtain a prediction probability. A monitoring network is constructed to integrate multi-modal geological data, resource allocation is optimized by combining edge calculation dynamic load balancing, geological risk prediction is realized by using a prediction model, the problems of single data, poor dynamic adaptability and weak model generalization ability of a traditional method are solved, the prediction precision is improved, the response time is shortened, and the prediction efficiency is improved. And a high-risk area is visually displayed through a three-dimensional risk thermodynamic diagram.

The invention belongs to the technical field of power equipment state monitoring and fault diagnosis, and discloses a high-voltage circuit breaker state quantity feature extraction method and system, and the method comprises the steps: extracting a local maximum value point and a local minimum value point from a denoised original monitoring signal; feature vectors are constructed and input into the trained neural network model, six-dimensional vectors are output in a Softmax output layer, and the six-dimensional vectors correspond to prediction probabilities of six orders in the optimal B-spline interpolation orders; based on the constructed envelope, adopting empirical mode decomposition to obtain a plurality of intrinsic mode function IMF components, performing Hilbert transform on each IMF component, constructing a state quantity feature set according to the instantaneous frequency and amplitude change of the analysis signal, and judging whether the high-voltage circuit breaker is in an abnormal state or not. According to the method, the vibration signals collected by the circuit breaker are subjected to multi-stage intelligent processing, high-precision and low-noise feature extraction is achieved, and fault diagnosis and state recognition of the circuit breaker can be effectively supported.

The invention provides a power distribution control cabinet fault response method and device, and relates to the technical field of electrical automation control, and the key points of the technical scheme are that a weighted directed graph model representing the relation of internal components of a power distribution control cabinet is constructed in advance; mapping component fault information predicted by the digital twin system to attributes of corresponding nodes in the weighted directed graph model; identifying a fault path or a fault cluster satisfying a predetermined condition; for each identified fault path or fault cluster, calculating a composite risk index according to the prediction probability of the nodes included in the fault path or fault cluster, the weight of the connection edge and the severity of the influence on the system; and generating early warning information according to the composite risk index. According to the power distribution control cabinet fault response method and device provided by the invention, a potential fault propagation path or a fault aggregation area can be identified based on physical, electrical and influence relationships among components, and a composite risk formed by a plurality of potential fault combinations is quantified, so that a preventive maintenance decision is guided.

The invention discloses an intelligent sound box synchronous coordination monitoring system and method based on artificial intelligence, and belongs to the technical field of intelligent sound boxes, audio signals, environmental parameters, user behavior data and network states are synchronously acquired through a plurality of intelligent sound boxes distributed at different positions, and the network states are subjected to timestamp calibration and standardization preprocessing to obtain a plurality of intelligent sound boxes; a multi-dimensional spatio-temporal data set is constructed, compressed and stored; inputting the preprocessed data into a graph neural network to construct a dynamic association graph, analyzing association among the data, recognizing a current use scene in combination with environment characteristics and a user behavior mode, evaluating a multi-loudspeaker-box cooperation state and a user behavior risk level, and generating a comprehensive evaluation result; based on the evaluation result, a collaborative optimization strategy of the multiple intelligent sound boxes is generated, and an audio transmission path and resource allocation are dynamically adjusted; and constructing a space-time prediction model, performing probability prediction on a future sound box cooperation state and a user behavior trend, and when the prediction probability exceeds a preset threshold value, triggering a grading early warning mechanism and executing a corresponding early warning strategy.

The invention relates to a multi-spectral diagnosis result credibility evaluation method. The method comprises the following steps: synchronously acquiring three-spectral images and environmental parameters of target equipment; the collected three-spectrum image is preprocessed; inputting the preprocessed image into the improved model to obtain a prediction probability distribution vector of fusion features and three-spectral-band branches; obtaining a prediction category and uncertainty entropy; carrying out confidence score calculation; judging whether to trigger a feedback condition; according to the method, the complementary characteristics of three spectrums of visible light, infrared light and ultraviolet light are utilized, and the CBAM attention module is introduced, so that key characteristics under different spectrums are enhanced, and the sensing ability of the model to an abnormal region is improved; the method achieves the prediction result confidence evaluation based on a Monte Carlo Dropout reasoning mechanism, effectively recognizes a low-confidence result, gives a diagnosis conclusion, can also evaluate the confidence, is suitable for deployment in key scenes such as a high-pressure valve hall, and facilitates the improvement of the overall safety and stability of a system.

The invention belongs to the technical field of artificial intelligence, and particularly relates to a multi-mode early cognitive impairment screening method and system based on medical history information.The method comprises the steps that a first electrode plate and a second electrode plate are arranged at the forehead position and the occipital position of the brain of a user to be detected in advance; the third electrode plate and the fourth electrode plate are arranged on the left side of the brain side by side, and the fifth electrode plate and the sixth electrode plate are arranged on the right side of the brain side by side. And obtaining a disturbance coefficient set of the to-be-detected user under the reference detection mode combination, inputting the disturbance coefficient set into a pre-trained early cognitive impairment screening model, and predicting to obtain a prediction probability value of the early cognitive impairment of the to-be-detected user, and by adopting the scheme to screen the early cognitive impairment, the accuracy is high, the universality is strong, and the accuracy is high. The method can be applied to screening of people in communities, nursing homes and the like on a large scale.

The invention provides a multi-parameter adaptive dynamic multilevel decision converter transformer fault diagnosis method and system, and the method comprises the steps: constructing a multi-parameter fuzzy feature space based on temperature, voiceprint, current and oil soluble gas parameter data, carrying out the statistics of fuzzy features through sliding time sequence segmentation, generating a multi-parameter fuzzy time sequence feature vector, and carrying out the fault diagnosis of a converter transformer. Obtaining multi-parameter internal cross characteristics and multi-parameter external cross characteristics, and performing splicing fusion to form multi-parameter correlation characteristics; constructing a multi-stage aggregation classifier, dynamically distributing a weight for each classifier based on verification set performance, fusing probability output fault types of each layer, and adjusting the matching degree of a model prediction probability and a real fault occurrence probability; and dynamically adjusting the depth of the hierarchy according to the matching degree adjustment condition, if the improvement of the fault diagnosis precision of continuous multiple layers is smaller than a set threshold value, terminating the expansion and retaining the current hierarchy, and executing multi-level processing on a prediction result. According to the invention, various parameter information is effectively fused, and the prediction accuracy and reliability are improved.

The invention discloses a three-dimensional point cloud geometric information compression method based on implicit neural representation, and the method comprises the steps: constructing a trunk structure of an implicit neural network through a plurality of sine representation network layers which are connected in series, and introducing a variable-scale position coding mechanism on this basis, the method enables a network to obtain higher geometric reduction precision while keeping a compression ratio, and comprises the following steps: (1) inputting space coordinates of divided voxels into a position coding module with adjustable scale parameters; (2) feeding a coding result into an implicit neural network constructed by a network layer based on sine representation, and outputting the occupancy probability of the voxel through an activation function; (3) in a training stage, the model continuously optimizes parameters, so that the output probability distribution is highly consistent with a real occupied label; (4) after model training is completed, a method of combining an AdaRound second-order quantization optimization strategy and quantization perception training is introduced, network weight is finely adjusted, and quantization errors are reduced; (5) in a reasoning stage, judging whether the voxel is occupied or not according to a preset threshold value, and when the prediction probability exceeds the threshold value, regarding the voxel as occupied; and (6) all voxels judged to be occupied are aggregated, and reconstruction of the geometric structure of the point cloud is completed.

The invention relates to a generalized energy storage and micro-grid collaborative low-carbon operation method based on a dynamic electricity price strategy. The method comprises the following steps: S1, constructing a micro-grid operator unified scheduling model framework and initializing an electricity price signal; s2, constructing a demand response model driven by user satisfaction, and obtaining a user load curve; s3, generating an uncertainty random disturbance scene set based on a wind and light power prediction probability density function; s4, establishing a generalized energy storage model, and respectively constructing a virtual energy storage model of entity energy storage and EV aggregation in the multi-microgrid; step S5, constructing a low-carbon optimal scheduling model for the flexibility resource collaboration of the microgrid group; s6, solving the multi-microgrid scheduling optimization model in a wind-solar disturbance scene, and extracting a tail scene mean value as a risk constraint target of scheduling optimization based on CVaR; and S7, the micro-grid operator corrects the direction of the electricity price signal based on a CVaR result, and iteratively updates in combination with a differential evolution algorithm until the target value tends to be stable.

The invention discloses a bacteriophage host prediction method based on a weighted integrated convolutional neural network. The method comprises the following steps: obtaining a joint feature vector for an input bacteriophage protein sequence and a host receptor protein sequence; taking the bacteriophage-host pair as a positive sample, randomly selecting non-host bacteria for each bacteriophage to generate a negative sample, dividing the negative sample into M subsets, and combining each subset with all the positive samples to form M balance training subsets; for each balance training subset, training an independent one-dimensional convolutional neural network model; independently training a one-dimensional convolutional neural network sub-model for each balance training subset to obtain a plurality of sub-models with different performances; and distributing weights based on the performance indexes of the sub-models on the verification set, performing weighted fusion on the prediction probabilities of the M sub-models, and outputting a final host interaction probability. The method realizes accurate prediction of phage-host interaction on the premise of only needing basic sequence information, and has the advantages of light weight, high efficiency and wide applicability.

The invention relates to a PRP preparation quality evaluation method based on double-branch image fusion, and belongs to the technical field of artificial intelligence and data processing. The method comprises the following steps: acquiring a centrifugal sample image data set, and dividing the centrifugal sample image data set into a training set and a test set; constructing a double-branch neural network, wherein the double-branch neural network comprises an adaptive contrast correction module, a dynamic convolutional layer, a deformable spatial pyramid pooling layer, a gated cross attention module, a Transform encoder and a classification layer; training the double-branch neural network by using data in the test set; in the training process, the dual-branch neural network is optimized through a loss function, and trainable parameters are updated by adopting a gradient descent updating strategy to obtain a trained network; and inputting data in a test set into the trained network to obtain output category prediction probability distribution, and taking the category corresponding to the maximum probability as an evaluation result. According to the method, the PRP preparation quality evaluation accuracy can be improved.

The invention relates to the field of identity association, and discloses a mobile terminal identity information association analysis method, system and device and a medium. The method comprises the following steps: acquiring a non-identification parameter set of a terminal to be judged and a stock parameter set of stock equipment; inputting the non-identification parameter set and the stock parameter set into a twin neural network model to obtain a prediction probability; when the prediction probability is greater than a preset probability threshold, determining that the to-be-determined terminal and the stock equipment are the same equipment, and associating the non-identification parameter set with the stock equipment; when the prediction probability is smaller than a preset probability threshold value, marking the to-be-judged terminal as a suspected new device, performing time sequence verification on the suspected new device and the stock device based on the dynamic time sequence parameter and the application behavior parameter, after verification is passed, re-judging that the to-be-judged terminal and the stock device are the same device, and if not, judging that the to-be-judged terminal and the stock device are the same device. And marking the to-be-judged terminal as a new device. According to the invention, the reliability and stability of association can be improved.

The invention provides a linkage control method and system for computer auxiliary equipment, and the method comprises the steps: generating an equipment linkage control instruction sequence through prediction probability distribution, managing a multi-equipment concurrency control request through a priority queue, and dynamically adjusting an instruction execution sequence according to the equipment response time and the resource occupation condition, if the response time of the equipment exceeds a preset delay threshold value, reducing the linkage priority of the equipment and activating standby equipment; and adjusting equipment linkage strategy parameters according to a model updating result, adopting an adaptive threshold mechanism to optimize a prediction trigger condition, calculating a user satisfaction index through a moving average algorithm, and if the satisfaction is lower than a reference value, backtracking and analyzing operation sequence features and recalibrating personalized operation habit model parameters.

The invention discloses a hepatocellular carcinoma postoperative early recurrence prediction method based on multi-modal fusion. The method comprises the following steps: firstly, integrating clinical data of a training set, a preoperative enhanced CT image and a postoperative full-view digital pathological image, and carrying out standardized correction; then, traditional image omics features and deep learning features are extracted from the CT image, cell nucleus morphological features and tumor microenvironment spatial configuration features are extracted from the pathological image, and key feature signatures are screened out through a maximum correlation minimum redundancy algorithm (mRMR) and LASSO regression in combination with clinical features. And then carrying out progressive model construction by adopting an XGBoost algorithm, sequentially establishing a clinical single-mode model, an image single-mode model, a pathological single-mode model and a multi-mode fusion model, and explaining and visualizing the models by utilizing an SHAP value and a Grad-CAM technology. Finally, the performance of the model is evaluated in a multi-dimensional mode through internal cross validation, foresight and external independent validation, risk layering is carried out based on the prediction probability, and individualized postoperative management is guided.

The invention relates to the technical field of brightness compensation, and discloses an OLED display screen brightness jump compensation method and system, and the method comprises the steps: obtaining real-time image data, user operation historical records and real-time environment brightness; standardizing the image data and the ambient brightness to obtain an initial data set; performing feature extraction by adopting a convolutional neural network, and determining a dynamic change trend; determining a brightness fluctuation parameter corresponding to the ambient brightness, and judging whether a brightness jump risk factor is generated or not in combination with the dynamic change trend; analyzing the matching degree of the image data and the user operation historical record to obtain a prediction probability value corresponding to the potential jump scene; predicting the brightness by using a long-short-term memory network, and correcting by combining a prediction probability value to obtain a brightness value in a future period of time; and generating a compensation strategy parameter set in combination with the ambient brightness and the brightness jump risk factor, thereby obtaining a brightness adjustment sequence. According to the method, a compensation strategy can be generated in advance, and non-perceptual smooth transition is realized.

The invention relates to the technical field of medical image processing, and discloses an ultrasonic image edge segmentation method and system, a terminal and a medium, and the ultrasonic image edge segmentation method comprises the steps: constructing an initial model, obtaining a training set, inputting the training set into the initial model, and obtaining a prediction probability graph; performing data preprocessing on the training set to obtain boundary supervision information; boundary sensing information is obtained according to the prediction probability graph and the boundary supervision information; obtaining a target model according to the boundary perception information, the training set and the initial model; and acquiring an echocardiogram of a user, and inputting the echocardiogram into the target model to obtain a segmentation probability graph. According to the method, the boundary monitoring information is provided by preprocessing the training set to solve the problem of boundary blur, the perceptual ability of the model to the boundary is enhanced through the boundary monitoring information, and the boundary segmentation precision of the ultrasonic image can be improved.

The invention discloses a video false information cross-domain detection method and system under a modal incomplete condition, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining available multi-modal data in video news samples from different sources; processing the available multi-modal data to generate a modal missing mask; projecting the available multi-modal data to a shared semantic space based on linear mapping to obtain shared semantics, and performing feature reconstruction on the shared semantics according to a preset residual self-encoder model and a modal missing mask to obtain complemented modal features; and processing the complemented modal features based on a hybrid expert mechanism to output the prediction probability of the false video. According to the method, the accuracy and the cross-domain adaptive capacity of video false information detection can be remarkably improved in a complex real scene with serious mode loss or remarkable field difference, and the method has good robustness, expandability and wide practical application prospects.

The invention relates to a natural language processing technology, in particular to a fine empirical tracing and declaration verification method based on heterogeneous target fusion, which comprises the following steps of: retrieving candidate documents related to a to-be-verified declaration, and extracting candidate sentences related to the to-be-verified declaration; constructing a cascaded statement-sentence association probability distribution model and a statement-sentence set association probability distribution model; learning prediction probability distribution between the declaration and a single candidate sentence based on a declaration-sentence association probability distribution model; screening a plurality of candidate sentences with relatively high empirical values to obtain a candidate empirical set; on the basis of a statement-sentence set association probability distribution model, capturing a semantic synergistic effect between candidate sentences in the candidate empirical set, and calculating global category probability distribution of the statement to be verified and the candidate empirical set; jointly optimizing parameters of the two models by adopting a heterogeneous target; and on the basis of the two trained models, fine empirical cause tracing and declaration verification are realized. According to the invention, a visual and accurate viewpoint verification result can be provided.