10results about How to "Training accurately" patented technology

The invention discloses a dynamic modeling analysis system for combustion atmosphere data of a biomass oxygen-enriched combustion boiler, and relates to the technical field of dynamic modeling analysis. The system comprises a working condition self-adaptive feature extraction module, a time sequence maintaining model training module, an online self-adaptive correction module and an incremental model updating module. Through a working condition adaptive feature extraction module, a time sequence maintenance model training module, an online adaptive correction module and an incremental model updating module, adaptive feature vectors are generated through feature extraction, a basic model is obtained through training, and online correction and incremental updating are performed by combining feature vector verification threshold judgment. And finally, iterative upgrading is completed through an incremental learning fine tuning model, the reliability of dynamic modeling analysis of the combustion atmosphere data of the biomass oxygen-enriched combustion boiler is improved, and the problem that in the prior art, the reliability of dynamic modeling analysis of the combustion atmosphere data of the biomass oxygen-enriched combustion boiler is low is solved.

The present disclosure provides a parameter prediction model training method, a parameter prediction method and device, comprising: obtaining first target data, the first target data comprising: historical application program interface (API) request parameter data and attribute data corresponding to the historical API request parameter data, the attribute data being used to describe parameter declaration of the historical API request parameter data, the parameter declaration comprising: parameter name definition and parameter value; determining a pre-training model based on the first target data, the pre-training model being used to perform first parameter prediction on an API request; obtaining second target data, the second target data being used to describe question and answer data pairs corresponding to a plurality of user questions, each user question being derived from the API request parameter data and the attribute data of the API request parameter data; and determining a parameter prediction model based on the second target data and the pre-training model. Thus, API parameter prediction is accurately performed.

The invention discloses a honeycomb-free large-scale MIMO unmanned aerial vehicle associated power control method and equipment based on reinforcement learning, and a medium, and relates to the technical field of wireless communication. The method comprises the following steps: acquiring parameters of a communication system containing multiple unmanned aerial vehicles and access points; a cellular-free large-scale MIMO transmission model is constructed; the method comprises the following steps: determining a state space, an action space and a reward function based on a cellular-free large-scale MIMO transmission model, and respectively constructing intelligent agents for an uplink power control coefficient optimization problem of an unmanned aerial vehicle in a system and an access point unmanned aerial vehicle clustering optimization problem; training the intelligent agent by using a depth deterministic strategy gradient reinforcement learning algorithm; and obtaining an uplink power control coefficient of the unmanned aerial vehicle in the system and an unmanned aerial vehicle clustering result of each access point by using the intelligent agent based on an optimized uplink power distribution strategy and an access point unmanned aerial vehicle clustering strategy. According to the method, efficient joint optimization of unmanned aerial vehicle access point association and uplink power control can be realized under a rapidly changing channel condition, and the method has relatively high practical practicability under a low-altitude economic background.

The invention relates to a model training method, a measurement method, a device, equipment and a program product. The model training method comprises the steps of performing edge detection on a to-be-recognized image based on an edge detection model to obtain an edge detection result; the to-be-identified image is a sample image to be labeled with a label; generating an initial label of the to-be-recognized image according to the edge detection result; adjusting the initial label according to the real edge condition of the to-be-recognized image to obtain a target label of the to-be-recognized image; and training a candidate detection model based on the sample image and the target label of the sample image until a training condition is reached, and obtaining a target detection model for edge detection. By adopting the method, the label generation efficiency and accuracy can be improved.

This invention discloses a method for predicting blasting vibrations based on neural networks, comprising: acquiring historical blasting data during blasting and preprocessing the blasting data to obtain training data; using a neural network as a blasting vibration prediction model, and based on the training data, training the blasting vibration prediction model using a multi-step optimization algorithm to obtain a trained blasting vibration prediction model; acquiring real-time blasting data during blasting, using this real-time blasting data as input to the trained blasting vibration prediction model, and using the trained blasting vibration prediction model to predict blasting vibrations to obtain blasting vibration prediction results. This invention not only eliminates the need for prediction personnel to possess specialized knowledge but also improves prediction efficiency. The multi-step optimization algorithm used to update the neural network not only achieves rapid local convergence but also avoids getting trapped in local optima, thereby achieving accurate training and enabling the trained neural network to better predict blasting vibrations.

This invention discloses an agent management method and system for low-code design. The method includes: acquiring multiple historical code build records of a user in a low-code platform; determining programming requirement parameters corresponding to each historical code build record based on a prediction model; creating a corresponding basic agent model based on the programming requirement parameters; training the basic agent model based on the historical code build records and the corresponding programming requirement parameters to obtain a code-enhancing agent; the code-enhancing agent is used to optimize the code generated by the user's operations on the low-code platform. Therefore, this invention can achieve accurate agent training based on historical builds and requirement analysis, improve the automation and accuracy of code optimization on low-code platforms, and reduce the risk of code errors caused by manual optimization.

The application discloses a model training method, an address positioning method, an electronic device, a storage medium and a computer program product, relates to the technical field of large models and image positioning, and the method comprises the following steps: acquiring a training data set, wherein the training data set comprises a training scene image and a visual question and answer data set of corresponding address information of the training scene image; performing cross-view alignment training on an initial multi-modal address positioning model by using the training data set, to generate an intermediate multi-modal address positioning model; and performing address positioning training on the intermediate multi-modal address positioning model by using the training data set, to generate a target multi-modal address positioning model, wherein the target multi-modal address positioning model is used for performing address positioning analysis on a target scene image to be processed and an address question, to obtain an address answer. The application solves the technical problems of the image address positioning scheme provided in the related art, such as coarse positioning granularity, poor positioning accuracy and poor flexibility of question and answer interaction.

A product quality prediction system based on federated learning includes a server and clients set up on multiple production lines. The server sends an initial product quality prediction model and fused model parameters to the clients. The fused model parameters are obtained by fusing the model parameters sent by each client. The clients include: multiple sensors for collecting product manufacturing process data; a training set construction module for acquiring sensor data and performing feature extraction to construct a local training set; a model training module for training the acquired product quality prediction model using a forward propagation federated training method based on the local training set; encrypting and sending the model parameters during training to the server and updating the local product quality prediction model according to the fused parameters; stopping training when training requirements are met to obtain a trained product quality prediction model; and a product quality prediction module for performing quality prediction based on the trained product quality prediction model.

The application discloses an intelligent driving real-time semantic segmentation method and system suitable for complex climates, and aims to solve the problems of low segmentation accuracy, poor real-time performance and hardware deployment of existing semantic segmentation methods under complex climates (rain, fog, snow, night, etc.). The application constructs a hardware-friendly single-branch CNN semantic segmentation network, proposes a generation denoising-weighted distillation training strategy, guides the CNN student branch to learn the invariance features of complex climates through the Transformer teacher branch, improves the model robustness and segmentation performance, and simultaneously optimizes the segmentation boundary and relieves the training imbalance problem by combining a boundary enhancement label generation module. The hardware acceleration system of the application is based on an FPGA hardware platform, adopts a software and hardware collaborative design, realizes efficient and real-time inference of the algorithm through a flow water reuse calculation architecture and a preprocessing module. The application can meet the intelligent driving real-time environmental perception requirements by taking into account the accuracy and speed in complex climate scenes.

The invention discloses an image classification model training method and device, an image classification method and device, equipment and a medium. The method comprises the steps that an original image is acquired, an image description text corresponding to the original image, N attribute description texts and corresponding saliency areas are determined, and N is larger than 2; performing mask processing on each saliency region, and determining N region mask images; feature coding fusion is carried out on the original image, the N area mask images, the image description text and the N attribute description texts, and fusion coding features are determined; and performing model training based on the fused coding features, and determining a target classification model. According to the method, cross-modal feature code fusion is carried out on the whole image and a saliency region to obtain a fusion feature code capable of accurately representing the image, accurate training of a model is realized through the fusion feature code, and a target classification model with relatively high image category recognition accuracy is obtained. Therefore, image classification with high precision can be carried out according to the target classification model.