7results about How to "Good calculation efficiency" patented technology

The invention discloses a social network link prediction-oriented time sequence diagram network parallel training acceleration method. The method comprises the following steps: firstly, calculating a redundancy score and an old score for each user interaction edge in a training set, and calculating a comprehensive information loss score according to the redundancy score and the old score; secondly, according to a preset core set retention proportion alpha, selecting all user interaction side comprehensive information loss scores and upper alpha quantiles of repetitiveness as threshold values, and discarding user interaction with the comprehensive information loss scores lower than the threshold values, so that a simplified core set is obtained through single-time preprocessing; then carrying out adaptive batch division on the obtained core set, and dynamically determining an acceptable maximum user interaction number in each batch according to a redundancy and old comprehensive information loss score; and finally, training the time sequence diagram neural network based on the divided batches to realize social network link prediction. The method not only improves the training efficiency, but also greatly improves the precision of the model.

This invention discloses a method for evaluating the anti-sliding stability of transmission tower pile foundations on slopes under rainfall conditions. The method decomposes the stability evaluation problem of transmission tower foundations under rainfall-induced landslide conditions into several sub-problems: First, the basic parameters of the slope and future rainfall information released by the meteorological station are obtained to determine the shear strength parameters of the slope soil after rainfall time t. Based on these parameters, a numerical method is used to determine the stability state of the slope. If the slope is unstable, the relative position of the foundation and the potential sliding surface is first determined. If the foundation is above the potential sliding surface, the transmission tower will fail. If the foundation is partially below the potential sliding surface, the rotation angle of the foundation is calculated based on the soil conditions at the bottom of the foundation. The stability of the transmission tower is then determined according to the maximum allowable tilt value of the transmission tower specified in the "Operation Regulations for Overhead Transmission Lines".

The application discloses an image classification method, system, device, medium and product based on Wasserstein discriminative dictionary learning, relates to the field of image classification, and comprises the following steps: acquiring a training data set; constructing a target function of Wasserstein discriminative dictionary learning based on the training data set; the target function comprises a reconstruction error term based on a Wasserstein distance, an improved local constraint term and an intra-class sharing term; an alternating iterative optimization algorithm is used to solve the target function, so that an optimal dictionary and an optimal coefficient matrix are obtained; a test coefficient vector is determined based on the optimal dictionary and an image to be classified; a category membership matrix of a dictionary atom in the optimal dictionary is constructed based on the optimal coefficient matrix; category scores of the image to be classified are calculated based on the test coefficient vector and the category membership matrix, and the category of the image to be classified is determined according to the maximum score. The application can improve the precision and efficiency of image classification.

The invention belongs to the technical field of blockchain security, and discloses an industrial Internet of Things anonymous responsibility-traceable data editing method and system based on a blockchain. The method comprises the following steps: generating a chameleon Hash trap door by a trusted mechanism, splitting the chameleon Hash trap door into shares by using threshold secret sharing, distributing the shares to verifier nodes, and initializing a dynamic accumulator; the owner encrypts the production data and formulates a modification strategy, and generates a hash value and an initial voucher by using chameleon hash and uploads the hash value and the initial voucher to a chain; the modifier initiates a request containing zero-knowledge proof, after verification is passed, the verifier node generates partial vouchers by using the trap door share, and the modifier aggregates the partial vouchers into a one-time voucher to complete editing; and the trusted mechanism decrypts the identity of the malicious modifier, moves the malicious modifier out of the dynamic accumulator and updates the revocation list. According to the method, trap door leakage is prevented through a threshold sharing mechanism, zero knowledge proof and a dynamic accumulator are combined, and effective tracking and permission revocation of malicious behaviors are realized while identity privacy of a modifier is guaranteed.

The application is based on a dual-flow gate violence detection method and system of an expanded 3D convolutional network and a Transformer, and realizes classification through the following steps; S1: obtaining a video stream, segmenting the video stream into frames, and calculating the dense optical flow between the continuous frames of the video stream; S2: segmenting the video stream segment through a sliding window, and suppressing non-motion elements in the RGB mode by using the optical flow mode; S3: performing spatiotemporal modeling on the dense optical flow and the SRGB mode by using a 3D convolutional neural network, and performing feature fusion to create a feature map; S4: performing time modeling on the spatiotemporal feature sequence of the video stream segment by using a Transformer encoder, and generating a time feature map; S5: performing average pooling by using a 1D convolutional neural network, and performing classification by using a multilayer perceptron, and classifying the video as violence or non-violence. The application solves the deficiency of the violence detection model in processing spatiotemporal features, improves the accuracy and efficiency of violence detection, and especially has an application effect in a dynamic video monitoring scene.

The application provides a motor target parameter estimation and long-time phase correlation accumulation detection method and system, which first carries out radio frequency conditioning and sampling on a radar original echo, carries out pulse compression after obtaining a discrete complex baseband signal, and outputs a range focusing but still containing range migration frequency domain signal. The core step utilizes a second order Keystone transformation to correct the second order range migration of the target, outputs a second order correction signal eliminating the range-azimuth coupling, and then searches the optimal speed through a Bayesian optimization and CPU parallel acceleration algorithm. On this basis, the IAR algorithm is utilized in combination with the optimal speed estimation value to correct the first order range migration, and phase compensation is carried out according to the speed matching phase factor to obtain a phase matching signal. Finally, the matching signal is subjected to a non-uniform fast Fourier transformation in a slow time domain to realize energy focusing, and the complete motion parameters of the target are solved through analyzing the peak position and amplitude characteristics in the Doppler domain.

This invention discloses a method and system for reinforcement learning-based backbone modules for remote sensing images in the fields of remote sensing image processing and deep learning. The method includes the following steps: input preprocessing and construction of a heterogeneous primitive library; heterogeneous feature alignment and normalization representation; construction of a dynamic routing decision space and generation of routing actions; updating the reward network and routing strategy based on bi-layer primitive optimization; stabilizing policy updates based on reference policy regularization; and performing dynamic feature aggregation, task prediction, and controllable inference. This invention can adaptively allocate computational resources according to the complexity of remote sensing image samples, significantly reducing redundant computation while ensuring performance in detecting complex scenes, small targets, and dense targets. Furthermore, a single model can adapt to deployment scenarios with different computational constraints, making it widely applicable in engineering.