7results about How to "Generate good effect" patented technology

This invention relates to the field of exercise prescription generation and recommendation technology, and discloses an LLM-based generative recommendation method for exercise prescriptions. The method involves acquiring and preprocessing data, constructing a knowledge graph based on the preprocessed data (S1), pre-training the LLM model, inputting the constructed knowledge graph into the LLM model, and optimizing it through reinforcement learning. The PPO algorithm is used to optimize the LLM model's output strategy. Based on the optimized LLM model, the exercise prescription is derived and generated in natural language using FITT parameters. This method combines user characteristics and scenario information to generate high-quality, personalized exercise prescriptions. The invention utilizes a multi-dimensional reward function framework to quantify prescription quality and optimize the generation strategy, ensuring that the generated exercise prescriptions are not only scientifically sound but also conform to the user's actual situation and preferences. Synthetic data is generated through a user simulator, achieving an upgrade from static recommendation to dynamic adaptation, improving user experience and the level of intelligence in exercise and health management.

The application discloses a diffusion model training method and a spatio-temporal data generation method and device based on a spatial knowledge graph, an equipment and a medium, and relates to the technical field of data processing. The diffusion model training method comprises the following steps: constructing a spatial knowledge graph according to node information in a target region; the target region comprises adjacent first and second sub-regions, and the spatial knowledge graph comprises a plurality of triples composed of a head entity, an inter-entity relationship and a tail entity, and the head entity belongs to a node in the target region; for each first head entity belonging to the first sub-region in the spatial knowledge graph, the neighborhood information of the first head entity is determined; the neighborhood information of each first head entity is aggregated respectively to generate a first embedding guide signal; and the diffusion model is trained according to the first spatio-temporal data of the first sub-region and the first embedding guide signal to obtain a target diffusion model, and the target diffusion model is used for generating second spatio-temporal data of the second sub-region. The target diffusion model can be used to realize a spatio-temporal data generation task for unknown geographical space.

This utility model discloses an electric foam-generating brush, relating to the field of cleaning equipment; it includes: a brush handle with a control switch; a brush head connected to one end of the brush handle, comprising: a base with a water tank at one end and a foam-generating motor and control module at the other end, and a foam-generating element on its side wall; the water tank is connected to the inlet of the foam-generating motor via a water supply pipe; the outlet of the foam-generating motor is connected to the foam-generating element; the control module is electrically connected to both the foam-generating motor and the control switch; and a squeegee head is located on the side of the base away from the brush handle. This application can automatically generate and dispense foam during cleaning, with good foam generation and high foam output, thereby improving cleaning efficiency and effectiveness.

This invention discloses a method and apparatus for identifying road changes using remote sensing imagery. The method includes: acquiring new and old remote sensing images of unlabeled roads; cropping the remote sensing images; comparing the cropped road images; inputting the compared road change images into a deep learning model for self-supervised training to obtain a pre-trained model; training the pre-trained model using labeled road remote sensing images to obtain a classification and recognition model; and inputting the desired road change images into the classification and recognition model by transferring the parameters of the self-supervised model to obtain the road change results. The method provided by this invention can intelligently identify road changes by eliminating the need for manual labeling, achieving high efficiency, and reducing costs, thus enabling efficient monitoring and dynamic supervision of road changes. This provides technical and data support for promoting traffic road maintenance and optimization, and implementing road hazard detection and remediation.

The application discloses a breeding method of yellow chicken with antler crown and beard, and comprises the following steps: S1, selecting yellow chicken with antler crown to self-cross, constructing paternal pure line P1; selecting yellow chicken with beard to self-cross, constructing maternal pure line P2; S2, performing positive and reverse cross of P1 and P2 as paternal and maternal lines respectively; forming F1 generation core group; S3, performing cross of chicken breeds in the F1 generation core group; through phenotype and gene detection, yellow chicken with homozygous GG type antler crown and homozygous MbMb type beard is reserved, forming F2 generation homozygous candidate group; S4, self-crossing chicken breeds in the F2 generation homozygous candidate group, obtaining yellow chicken strain A with antler crown and beard characteristics; through the above method, yellow chicken with distinct antler crown and beard characteristics can be bred, so that the ketone body of yellow chicken after slaughtering also exhibits obvious characteristics of antler crown, the technical problem that the product control and traceability system after yellow chicken slaughtering is ineffective is solved, and the working efficiency of yellow chicken slaughtering is greatly improved.

The application provides a multi-track music generation method and device, the method comprising: modifying a compound word structure; generating a compound word sequence using MIDI file data and the modified compound word structure; the compound word sequence comprising a word element sequence of instrument attributes; inputting the compound word sequence into a trained improved Transform neural network model to obtain multi-track music. The application adds instrument attributes to the compound word sequence by modifying the compound word structure, which is conducive to the generation of multi-track music, and the improved Transform neural network model is used to generate multi-track music with better effects.

This application discloses a method for generating personalized paper-cut faces based on constrained recurrent generative adversarial networks (RBANs), comprising: S10: establishing a face dataset including face images and a paper-cut dataset including paper-cut images; S20: using a pre-trained model to obtain a corresponding face parsing dataset based on the face dataset, wherein the face parsing images in the face parsing dataset are distinguished by black and white to distinguish key regions and non-key regions; S30: designing a recurrent adversarial network for fusing key facial region features; S40: training the recurrent generative adversarial network using the face dataset, the paper-cut dataset, and the face parsing dataset, and obtaining a face-generating paper-cut generator model that fuses key facial regions after the recurrent generative adversarial network for fusing key facial region constraints reaches stability; S50: inputting the face images and face parsing images into the face-generating paper-cut generator model to obtain personalized paper-cut faces.