8results about How to "Avoid training" patented technology

The application discloses a kind of secondary frequency control method, device and storage medium based on neural network, belong to microgrid control technical field, method includes: obtaining the rated angular frequency of distributed microgrid preset, and the active power of each distributed power generation unit;Active power is respectively input into the corresponding pre-trained neural network, and the frequency compensation of each distributed power generation unit is output, the frequency deviation caused by droop control is compensated, so that the angular frequency of distributed microgrid output recovers to the rated angular frequency preset;Corresponding pre-trained neural network refers to the neural network that each distributed power generation unit corresponds one training process and is added privacy protection processing, so that attacker cannot infer whether single sample is used for the training of neural network according to neural network output result.The application can improve the security and stability of microgrid, solve the problem that traditional secondary frequency control method exists privacy leakage risk and security risk.

The application discloses a kind of customized training plan generation methods for operation and maintenance personnel based on performance prism, which comprises the following steps: collecting multi-source heterogeneous demand data of operation and maintenance personnel on multiple platforms, and combining Carnot model and importance performance analysis model for fusion processing, extracting key technology theme through cluster analysis, drawing core training intention and decomposing into key measurement indicators, collecting employee daily operation and maintenance behavior data and calculating ability membership degree vector, combining post demand information, calculating target ability information to be enhanced, generating training content using intelligent workflow engine according to target ability information and key measurement indicators, generating hybrid training plan combining online and offline according to employee historical schedule, issuing the plan to terminal and setting task reminders, which can ensure that training is executed on time, improve training coverage and completion rate, and realize the intelligentization, customization and high efficiency of operation and maintenance personnel training from demand analysis, capability assessment, content generation to plan execution.

The embodiment of the invention provides a reliability detection and model automatic training method for a sealing structure of a wellhead. The method comprises the steps of obtaining a size data set of the sealing structure; the size data set comprises a plurality of size data; the size data represents information of a physical structure of the sealing structure; performing matching processing on the size data set and a data set in a preset model library to obtain multiple groups of matched data sets; determining an initial model corresponding to the matched data set based on a preset model library; based on a Bayesian optimization algorithm, processing the size data set and each initial model to obtain a reliability detection model; the reliability detection model is used for processing the size data of the sealing structure to obtain a reliability detection result of the sealing structure. The method is used for achieving the effect of accurately and effectively detecting the reliability of the sealing structure.

The invention discloses a long video question-answering method and system based on a visual language model and a causal reasoning tree. The method comprises the following steps: carrying out time sequence segmentation on an input long video; calling a visual language model to generate text description for each fragment; analyzing the text description into a causal unit; leaf nodes are constructed, then intermediate nodes and root nodes are constructed, and a hierarchical causal reasoning tree is formed to construct a lightweight index for each node; user questions are received; and executing a search strategy on the tree by the question and answer agent, and integrating information to generate a final answer. The system comprises three main modules including a video analysis module, a fruit tree construction module and a question and answer agent module, and the steps of the method are completed. According to the method, a training-free framework completely based on the pre-training model is adopted, fine tuning can be carried out, the effect is further improved, the problem that the information retrieval accuracy of a single visual language model in a long video understanding task is low is effectively solved, and the answer accuracy of problems of global and local contents is improved.

The invention discloses a data expansion and exoskeleton joint end-to-end torque estimation method based on a diffusion model, and the method comprises the steps: carrying out the normalization processing of time series data from a multi-source sensor, carrying out the fragmentation according to a fixed length, and constructing a training sample with a motion class label; then, training a classifier-free conditional diffusion model by adopting a sample, and simultaneously learning conditional and unconditional denoising mapping relationships in a manner of randomly inactivating category conditions; in the generation stage, based on a classifier-free condition guidance mechanism, multi-modal time series data with specified motion category features are gradually generated from random noise; and finally, fusing the generated synthetic data with real acquired data to train a joint torque end-to-end prediction network, thereby realizing joint torque estimation of input sensor time sequence data. The method improves the prediction precision, generalization ability and stability of the end-to-end torque estimation model in a multi-action and few-sample scene, and has a good engineering application value.

The application discloses a hierarchical three-dimensional Gaussian semantic field construction method fusing a multi-modal large language model. The application comprises the following steps: firstly, generating an initial point cloud of a scene based on an obtained two-dimensional RGB image sequence, and then generating an initial three-dimensional Gaussian distribution set; then, respectively performing multi-level granularity segmentation to obtain original segmentation images corresponding to different granularity levels of each RGB image; for each granularity level, generating cross-view consistent segmentation images and pixel-level semantic images corresponding to different granularity levels of each RGB image; then, optimizing the three-dimensional Gaussian distribution set to obtain an optimal Gaussian distribution set containing semantic features of the current granularity level; and finally, constructing a hierarchical three-dimensional Gaussian semantic field containing different semantic granularities. The application realizes the construction of a three-dimensional Gaussian semantic field with high fidelity, multiple granularities and support for complex open vocabulary queries, has high robustness and real-time performance, and is suitable for fields such as scene understanding and virtual reality editing.

This application provides a data processing method, a data processing apparatus, an electronic device, a computer-readable storage medium, and a computer program product; the method includes: obtaining a first model state corresponding to a first checkpoint; the first model state is the model state output by a streaming processing engine after calling a large model to input first task processing data and performing the Nth model training; obtaining second task processing data of the streaming processing engine, where N is a positive integer; inputting the second task processing data and the first model state into a large model for training, and obtaining a second model state corresponding to a second checkpoint output by the large model; the second model state is the model state output after performing the N+1th model training on the large model.

This invention provides an efficient face style transfer method, apparatus, and device based on a diffusion model. The method includes: acquiring a face image to be stylized and a target style image; inputting the face image to be stylized and the target style image into a pre-trained style model for face stylization processing to obtain a final face style image; the pre-trained style model is constructed using a cross-attention mechanism based on a first basic style transfer model, a second basic style transfer model, a diffusion model, and a text prompting model; the first basic style transfer model is used to acquire style feature information of the target style image; the second basic style transfer model is used to acquire facial fusion information of the face image to be stylized and the target style image. By using a pre-trained model, processing efficiency is improved; and by combining the style transfer model, diffusion model, and text prompting model for processing, the realism and naturalness of the final face style image are improved.