7results about How to "Reduce computing resources" patented technology

This invention discloses a method, apparatus, device, and medium for malware detection based on subspace ensemble learning. The method includes: acquiring the installation package file of the software to be detected; parsing the installation package file to obtain the application programming interface (API) call relationships of the software to be detected; determining the API call relationship graph based on the API call relationships; filtering each API in the call relationship graph to obtain a target API set; and determining the software detection result of the software to be detected based on the target API set and a feature subspace ensemble learning model. This invention obtains the target API set by filtering the API call relationships and employs a feature subspace ensemble learning model composed of multiple machine learning algorithms, which can capture malicious behavior in software from different perspectives, thereby improving the accuracy of malware detection.

This invention provides a method for simulating buckling of subsea pipelines based on the pipeline element method, addressing the problems of low computational efficiency, complex modeling, and low accuracy in existing methods. Based on Euler-Bernoulli beam theory, this invention models the pipeline as a series of efficient pipeline elements. Internal pressure is converted into equivalent temperature rise, and the effective thermal expansion force generated by the effective temperature rise is applied to both ends of the pipeline element to construct the total potential energy equation. The second derivative of the total potential energy equation is performed to obtain the tangent stiffness matrix of the pipeline element. An updated Lagrange method is used for coordinate transformation. The secant relationship is used to calculate the pipeline element and soil resistance. A Newton-Raphson incremental-iterative numerical program is established to perform nonlinear analysis of the pipeline, progressively solving for the deformation and stress of the pipeline under complex loads. This invention significantly reduces the number of elements, improves computational efficiency, and accurately simulates nonlinear pipe-soil interactions. It is applicable to various pipelines, including short pipes, long pipes, and corroded pipes, as well as complex working conditions such as high temperature and high pressure.

The application relates to the technical field of industrial anomaly detection, and discloses a rotation-invariant multi-prototype industrial anomaly detection method, which comprises the following steps: S1: obtaining an industrial image to be processed and a corresponding defect region frame-level label, and constructing a foreground mask and a background mask based on the frame-level label; S2: inputting the industrial image into a pre-trained feature extraction network, obtaining an initial feature map, and performing channel mapping and normalization processing on the initial feature map to obtain a normalized feature map; and S3: rotating the normalized feature map at least two different angles to obtain a feature map in each rotation direction. The rotation-invariant multi-prototype industrial anomaly detection method is based on a feature extraction network pre-trained on a large-scale data set, can make a heat map generation module converge only through a small number of training rounds, does not need to train a complex model from the beginning, and greatly reduces data demand, computing resource and training time cost.

The present application belongs to the technical field of electrochemical catalysis, and provides a method for predicting the catalytic performance of tubular iron-nitrogen-carbon for CO2RR with different curvatures, which is mainly used for predicting the influence of different curvatures on the catalytic performance of tubular iron-nitrogen-carbon for CO2RR, and the main scheme comprises the following steps: a half-pipe iron-nitrogen-carbon structure model is established to replace the whole-pipe structure model to preliminarily screen the curvatures, and the feasibility and advantages of this method are explored; based on the screened iron-nitrogen-carbons with different curvatures, the influence of the curvatures on the catalytic performance of the iron-nitrogen-carbon for CO2RR is analyzed; and the influence of the curvatures on the electronic properties of the iron-nitrogen-carbon system is analyzed based on Bader charge. Under the premise of ensuring the accuracy of the method, the present application saves the calculation resources, predicts the influence of different curvatures on the catalytic performance of the tubular iron-nitrogen-carbon for CO2RR, explores the influence law of the curvatures on the catalytic activity and selectivity of the iron-nitrogen-carbon catalyst, and can be used for guiding the subsequent experimental research work.

This invention discloses a hierarchical multi-label attribution method and system that integrates atomic rule-driven credible features and knowledge distillation, belonging to the field of natural language processing technology. First, this invention constructs an atomic rule base for weakly supervised text annotation. Then, it uses a large language model as a teacher model to correct and supplement the weak annotation results, extracting the probability distribution of soft labels and intermediate layer feature representations on each level of labels. Next, it evaluates the credibility of the teacher model's output, selecting a subset of credible soft labels and credible feature dimensions. Then, it constructs a student model with a hierarchical output structure, designs a joint loss function, and distills the student model for training. Finally, it deploys only the student model for inference, outputting hierarchical multi-label attribution results and key evidence fragments. This invention, through the combination of atomic rules and credible knowledge distillation, significantly reduces inference costs while improving the accuracy, stability, and interpretability of hierarchical multi-label attribution.

The application provides a generator control method and device, computer equipment and a readable storage medium. The method comprises the following steps: obtaining an out-of-limit condition matrix of a specified line set; determining an out-of-limit line in the specified line set when each power transmission line fails based on the out-of-limit condition matrix; determining a target generator related to current supply of the out-of-limit line in a generator set corresponding to the specified line set; determining a power regulation upper limit set of the generator set based on a power regulation upper limit value of the target generator; generating an output constraint range of the generator set based on the power regulation upper limit set of the generator set corresponding to each power transmission line; and determining an optimal power of each generator in the generator set in the output constraint range through a preset particle swarm algorithm. The technical scheme can reduce the iteration number of the particle swarm algorithm, reduce the time cost and computing resources consumed by searching, and improve the accuracy of the calculation result.

This invention discloses a method and system for expanding underwater image datasets based on a generative model. The method first constructs a pairing dataset of side-scan sonar images and text descriptions. After encoding the images into the latent space and adding noise, the LoRA module is used for fine-tuning while freezing the parameters of the basic diffusion model, enabling the model to learn the bright areas, shadow areas, and background texture features in the side-scan sonar images. Subsequently, new samples are generated based on preset prompts, and these generated samples are filtered and fused with the original samples to form an expanded dataset. This approach can improve the structural diversity and style consistency of side-scan sonar training samples, reduce data acquisition and annotation costs, and improve the training effect of downstream target detection models.