9results about How to "Comprehensive forecast" patented technology

The application discloses a kind of multi-modal rumor detection method and system based on layered attention network, text feature vector is extracted by layered attention model, visual feature vector in news picture is extracted by VGG19 model, and news sample is judged as rumor or non-rumor according to the text feature vector and visual feature vector;Layered attention model includes word encoder and sentence encoder based on self-attention mechanism, the importance between current word and context word and the importance between current sentence and context sentence are measured, word vector representation and sentence vector representation that fuse context information are obtained, so that the features of news document hierarchy can be captured;The importance of the feature vector of each picture different area relative to text is measured by attention mechanism, which can make the model focus more on the picture area with higher importance, improve the detection accuracy of the model, and multi-modal detection is more suitable for multi-modal form news, to get more comprehensive and accurate rumor prediction.

The invention belongs to the technical field of gas reservoir exploration, and particularly relates to a method and a system for depicting a water invasion dominant channel space of a fractured-porous gas reservoir. The method comprises the following steps: establishing a dual-medium fine geological model, wherein the dual-medium fine geological model comprises a matrix porosity model and a discrete fracture network model; obtaining a matrix permeability model according to the matrix porosity model, and obtaining a fracture permeability model according to the discrete fracture network model; matrix heterogeneity characterization parameter gridding is carried out based on the matrix permeability model, and fracture heterogeneity characterization parameter gridding is carried out based on the fracture permeability model. According to the method, the near-wellbore zone water invasion channel or the seepage barrier can be accurately judged, the whole gas reservoir water invasion channel or the area to be subjected to water invasion can be predicted, the influence of human factors is reduced, and the method is visual, fine, high in calculation efficiency and high in accuracy for the crack-pore type reservoir. And predicting the geometrical morphology and spatial configuration relation of the water invasion channel with comprehensive evaluation parameters.

According to the method for predicting the drum strength of the vanadium-titanium sinter, the drum strength of the vanadium-titanium sinter is predicted through three components of TiO2, SiO2 and CaO in a vanadium-titanium sinter mixture, and the drum strength of the vanadium-titanium sinter is predicted through the interaction effect of TiO2, SiO2 and CaO added in prediction. And the interaction influence of TiO2 and R and the influence of SiO2 + CaO on the drum strength of the vanadium-titanium sinter are fully considered, so that the prediction is more comprehensive, scientific and accurate.

This application provides a method, apparatus, storage medium, and processor for fault prediction of power modules. The method includes: acquiring historical operating data of the power module under multiple states; analyzing and selecting historical operating data that meets preset conditions as positive sample data; processing the positive sample data and inputting the processed positive sample data into a fault prediction data model to train the fault prediction data model and obtain a trained fault prediction data model; acquiring real-time operating data of the power module to be predicted; inputting the real-time operating data into the trained fault prediction data model to output the fault type of the power module to be predicted through the trained fault prediction data model, thereby realizing state monitoring and fault prediction of internal components of the power module, constructing an accurate fault prediction data model, and timely and accurately predicting potential faults of the power module, with more comprehensive predictions and a greater reduction in losses caused by faults.

The present application relates to the field of image processing, and specifically to an intestinal necrosis feature recognition system based on image analysis and a storage medium, wherein a computer program in the storage medium performs the following steps: obtaining medical images of a patient and dividing them into an expiration phase subset and an inspiration phase subset; performing spatial registration and motion artifact correction on the expiration phase subset and the inspiration phase subset to obtain multi-modal images; extracting a blood vessel obstruction feature, a perfusion injury feature and a cell necrosis feature in the multi-modal images; constructing a causal hierarchical relationship among the blood vessel obstruction feature, the perfusion injury feature and the cell necrosis feature based on an intestinal necrosis pathological mechanism; and determining a risk level of the patient having intestinal necrosis and a probability corresponding to the risk level based on the causal hierarchical relationship, the blood vessel obstruction feature, the perfusion injury feature and the cell necrosis feature using a random forest algorithm. The present application solves the problem of insufficient accuracy in recognizing intestinal necrosis features in the prior art.

The application relates to an oral squamous cell carcinoma auxiliary chemical immunotherapy response method and device, which comprises the following steps: acquiring image data and baseline clinical factors of a target user, the image data comprising metabolic parameters and image features of a tumor region; performing clinical parameter standardization processing on the metabolic parameters to obtain a standardized metabolic parameter set related to treatment response; performing stability screening on the image features by using an intragroup correlation coefficient to obtain key features related to treatment response; performing regression analysis on the baseline clinical factors to identify independent prognostic factors related to treatment response in the baseline clinical factors; and performing feature fusion on the standardized metabolic parameter set, the key features and the independent prognostic factors to obtain a response rate and a prognosis prediction result of the target user. The method can more accurately and comprehensively predict the response rate and the prognosis of the target user to the auxiliary chemical immunotherapy.

This invention relates to the field of battery testing technology, and more particularly to a multi-channel battery charge-discharge testing system and method. The method includes the following steps: acquiring voltage data of each battery cell in a multi-channel battery; performing voltage gradient stratification processing on the voltage data of each battery cell to divide the battery cell into multiple voltage intervals; detecting the number density of battery cells within each voltage interval to generate a voltage gradient distribution map; collecting the current distribution coefficients at each connection point of the multi-channel battery; performing current distribution difference analysis on the current distribution coefficients to calculate the actual current distribution ratio of each cell at different charge-discharge stages, generating current distribution difference data; and collecting overall temperature change data of the multi-channel battery during the charge-discharge process, as well as local temperature data of each battery cell. This invention, through battery testing technology, enables the analysis and prediction of the charge-discharge performance of multi-channel batteries, thereby improving the accuracy of multi-channel battery charge-discharge performance evaluation.

The application relates to the technical field of machine learning, and particularly discloses a risk prediction-based decision generation method and device, equipment and a storage medium. The application combines health risk parameters and market risk parameters to perform health risk prediction, so that the risk prediction is more comprehensive and accurate. The application also predicts medical expenses and adjusts predicted income according to the health risk prediction result, so that the predicted expenses and the predicted income are more accurate. Furthermore, the application obtains a target investment decision according to the predicted expenses, the predicted income and the predicted health risk, thereby improving the accuracy of the target investment decision. When the method is applied to medical and pension businesses and financial and insurance businesses, a reasonable and reliable target investment decision can be obtained, and the health risk hedging capability is improved.

The application discloses a kind of heterogeneous double mechanical arm end pose mapping method, system, device and storage medium, method includes the following steps: S1: obtaining the angular velocity of each joint of main mechanical arm, position and end attitude information, the spatial position and attitude of main mechanical arm end are calculated;S2: the motion trajectory data in the teaching process of main mechanical arm is collected, long short-term memory network model is established, the pose of future time step is predicted, and the motion trajectory of slave mechanical arm is planned;S3: data information is transferred, and communication delay problem is solved;S4: the pose information of main mechanical arm end is solved, joint angle is adjusted, and error is adjusted in combination with proportional-integral-derivative control;S5: position and velocity signal are filtered, and mechanical arm dynamics model is established, adaptive control law is designed, and trajectory smoothing algorithm is used;S6: virtual obstacle map is established, and safe working space is limited.The application realizes the comprehensive, accurate mapping and prediction of the end pose of heterogeneous double mechanical arm.