30results about How to "Generate good effect" patented technology

The invention discloses a robot visual identification, positioning and grabbing method based on RGBD point cloud, and the method comprises the following steps: 1, carrying out the camera point cloud data processing, synthesizing an RGB image and a depth image into a point cloud image, and carrying out the filtering and denoising processing of the point cloud data; step 2, target detection and capture attitude generation: performing target detection and instance segmentation on the RGB image, identifying a target object and generating a corresponding mask frame, and applying a mask to the depth image to extract depth point cloud data corresponding to the target object; obtaining the depth point cloud data, generating a plurality of groups of grabbing postures, and screening out an optimal grabbing posture; and thirdly, the mechanical arm grabbing execution module receives the optimal grabbing posture information output by the target detection and grabbing posture generation module and controls the robot to complete the grabbing action.

The invention discloses an image generation method and system based on diffusion model adaptive reasoning. The method comprises the following steps: constructing a next noise intensity prediction module TPM; improving an original diffusion model, and adding a TPM; the improved diffusion model is trained by using a reinforcement learning method, the whole denoising process is expanded for multiple times, the next time step is predicted in each iteration, an award is calculated according to the quality of a finally generated image and the number of steps, and TPM parameters are updated by using a PPO loss function; in the actual reasoning process, noise is randomly sampled from Gaussian noise and input into the trained diffusion model, the diffusion model is used for predicting a denoising result, meanwhile, the TPM is used for outputting distribution of time steps based on next denoising, and a distribution mode is used as the next time step used in actual reasoning; and when the predicted time step is smaller than a set threshold value, a final image generation result is obtained based on model output. According to the method, the image generation process can be dynamically adjusted, and a reasoning result with both efficiency and quality is realized.

The invention discloses a multi-modal prompt learning method based on retrieval enhancement, which can align a text encoder and a visual encoder of VLMs at the same time, adopts a retrieval enhancement strategy, captures cross-modal information through a cross-attention mechanism, realizes efficient multi-modal interaction, generates an adaptive embedded prompt based on the cross-modal information, and improves the learning efficiency. A multi-modal representation space is flexibly aligned, a learnable vector library (LTL) is introduced, the prompt generation effect is further optimized, excellent performance in various scenes is ensured, the accuracy of self-adaptive prompt can be enhanced through a retrieval enhancement strategy and a cross-modal cooperative sensing technology, efficient interaction of multi-modal information is achieved in combination with the learnable vector library, and the self-adaptive prompt effect is improved. Computing resources needed by fine tuning are reduced, the generalization ability of the model and the processing performance of complex tasks are remarkably improved, and a new solution is provided for efficient adaptation and optimization of large-scale pre-training VLMs.

The invention relates to the technical field of natural language processing, in particular to a method and system for achieving intelligent number asking based on a large model, and the method comprises the following steps: integrating data from various data sources including a database, a data warehouse, WEB service and Excel files; the data is cleaned, converted, standardized and normalized through the data management center, so that the data quality is ensured, and basic data support is provided for subsequent intelligent question analysis; the beneficial effects are that the method for realizing intelligent number asking based on the large model provided by the invention responds to complex and changeable data requirements in real time, helps an enterprise to quickly make a data-based decision, and reduces the time cost of waiting for the assistance of an IT department or a data analysis team; through effective integration and intelligent search of mass data resources, various data in an enterprise can be fully mined and utilized, and idleness and waste of the data resources are avoided.

The invention discloses a prebiotic composition capable of proliferating bifidobacterium and application of the prebiotic composition. The prebiotic composition comprises polydextrose, isomaltooligosacharide and galactooligosaccharide. Wherein the mass ratio of the polydextrose to the isomaltooligosacharide to the galactooligosaccharide is (0-2): (3-5): (4-5). The prebiotic composition provided by the invention can increase the content of total bifidobacteria in intestinal tracts, especially can specifically proliferate bifidobacteria longum and bifidobacteria breve, and can accurately regulate and control intestinal flora, thereby accurately intervening in host health.

The invention discloses an image generation method and system, electronic equipment and a computer program product, and relates to the field of large model technology and image processing. The method can comprise the steps that an initial scene image to be processed is determined, and the image content of the initial scene image comprises at least one display object; determining an auxiliary object image to be drawn at a target position in the initial scene image; based on the auxiliary object image, the target position and the initial scene image, a posture image and an object mask image of the auxiliary display object are determined, the posture image is used for representing the posture of the auxiliary display object, the object mask image is used for representing an image area where the auxiliary display object is located in the initial scene image, and the image area comprises the target position; and analyzing the attitude image, the object mask image and the initial scene image by using the redrawing model to obtain a target scene image. The technical problem of low image generation accuracy is solved.

The invention discloses a binary optimization method supporting multiple platforms, and the method comprises the following steps: 1, starting a target application load in a process, and collecting and analyzing program dynamic behavior information in the process; 2, taking the program dynamic behavior information and the target application load obtained in the step 1 as input, disassembling a corresponding binary code, optimizing, and outputting an optimized application load after linking is completed; and 3, replacing the original code of the application load with the optimized code of the application load to realize online code replacement. The invention further discloses a system for realizing the binary optimization method. The system comprises a program dynamic behavior information acquisition module, an offline binary optimization module and an online code replacement module. The invention further discloses application of the binary optimization method or the binary optimization system in multi-platform binary code optimization.

The invention relates to a poem and blessing generation method based on a large model, and the method comprises the steps: 1) carrying out the preprocessing of poem and blessing data, and forming a large-scale training data set; 2) using a large pre-training language model as a generative model, and pre-training the generative model to obtain a pre-trained generative model; 3) constructing training data of instructions, poem sentences and blessings, and performing instruction fine tuning on the pre-trained generation model by using the training data to obtain a poem sentence and blessings generation model; and 4) directly inputting the poem and blessing theme and the instruction into the poem and blessing generation model to generate the poem and blessing related to the poem and blessing theme. According to the method, the processing links of theme extraction and keyword extension during input are removed, a large model is adopted, and instruction fine tuning adapting to the fields of poems and blessings is added, so that extraction and addition errors are avoided, the quality of the generated poems and blessings is ensured, and the poems and blessings meeting requirements can be generated according to instructions.

The invention discloses a large language model system for document question and answer and generation and version comparison, which comprises a document question and answer module, a document generation module and a document comparison module which are connected in sequence. And the document question and answer module is used for performing content retrieval according to questions proposed by the user for the document after the user uploads the document, and providing direct answers or general answers. In this way, for a long document uploaded by a user, expected content can be quickly found, and corresponding content is provided for the page for checking. And the document generation module is used for calling a fixed template to automatically generate a document according with a specific structure according to user requirements. And the document comparison module is used for comparing different versions of the same document, accurately extracting a modified part, performing highlight display and displaying a comparison table through a chart. The method has the advantages that the document conforming to the template is automatically generated, the differences between different versions are accurately compared, errors in manual operation are reduced, and the working efficiency is remarkably improved.

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.

The invention relates to the field of medicine and cosmetology, and discloses a cell active peptide composition for promoting regeneration of multi-type collagen structure protein, a preparation method of the cell active peptide composition and application of the cell active peptide composition in medicine and cosmetology. The composition is prepared from the following raw materials: tripeptide-10 citrulline, hexapeptide-9, hydrolyzed sodium hyaluronate, gamma-aminobutyric acid, decarboxylated carnosine hydrochloride, acetyl tetrapeptide-3, acetyl tetrapeptide-11, palmitoyl dipeptide-7, palmitoyl pentapeptide-4 and the like. The composition can promote the synergistic expression of I-type, III-type, IV-type, VII-type and XVII-type collagen, fibronectin, elastin and glycosaminoglycan of the skin; collagen crosslinking is prevented, a triple-helix structure is maintained, and collagen degradation is inhibited. When being applied to skin medicine and beauty treatment, the composition can accelerate regeneration of structural proteins such as skin collagen, repair skin injury, reduce wrinkles such as crow's feet, lacrimal ducts, nasolabial folds and subocular wrinkles, solve the problem of skin aging caused by loss of the structural proteins such as collagen, and improve skin elasticity and compactness.

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.

This invention proposes a transferable adversarial attack method based on generative adversarial networks (GANs). This method introduces an auxiliary generator into the generator to improve generation quality and sample diversity, and uses a pyramid attention mechanism to focus on features at different scales. Before the generator network, auxiliary information is first generated by the auxiliary generator network. This generated auxiliary information is then concatenated with the original image, and the concatenated input image is fed into the generator network. To further improve the performance of the generator network, a pyramid attention module is introduced, allowing the network to focus on important regions and features of the image at different scales. Through a series of neural network layers and transformations, a high-resolution, realistic image containing perturbations is generated. Such an image has a higher success rate in black-box adversarial attacks and increases the diversity and quality of adversarial samples. Compared to existing black-box attack methods, the adversarial samples generated by this scheme are more realistic and effective, have a higher success rate, and possess a certain degree of transferability. This invention provides an effective solution for adversarial attacks in black-box scenarios.

The invention discloses a clinical research report automatic writing method and system, electronic equipment and a storage medium, and relates to the technical field of artificial intelligence. The method comprises the following steps: acquiring a user demand data set; performing semantic prediction training on a preset base model based on the user demand data set to obtain a semantic prediction model; acquiring a clinical report data set; training the semantic prediction model based on the clinical report data set to obtain an initial report generation model; obtaining a preset report generation score data set; training the initial report generation model based on the report generation score data set to obtain a final report generation model; obtaining a user report generation demand; and inputting the user report generation demand into the final report generation model, and outputting report content. According to the technical scheme, the content of the generated report can be dynamically adjusted according to user requirements.

The present invention discloses a quinoline derivative and an organic electroluminescent device comprising the same. The general structural formula of the quinoline derivative is shown in Formula I, wherein Ar1-Ar 10 Each independently represents hydrogen, deuterium or a group shown in formula II, and at least one represents a group shown in formula II. By utilizing the six-membered stable chelate ring formed by the quinoline skeleton, on the one hand, the stability of the complex can be improved, the doping effect can be enhanced, and the electron injection ability can be improved. On the other hand, after the stability of the complex is improved, it is more difficult for metal ions to diffuse, avoiding the material degradation and life reduction caused by the diffusion of metal ions to the hole end. The substitutable conjugated rigid heteroaryl structure introduced into the structure of the quinoline derivative of the present invention makes the material have a smaller electronic reorganization energy, which is beneficial to improve the mobility, so that the electrons in the charge generation layer can be quickly transmitted to the adjacent layer, thereby improving the device efficiency. #imgabs0#I;#imgabs1#II.

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 invention provides an image generation method based on a diffusion model, and relates to the technical field of image processing, and the method comprises the steps: constructing and initializing an initial diffusion model applied to the field of image generation, and carrying out the binarization processing of the initial diffusion model; replacing all binary structures of the diffusion model subjected to binarization processing with a time-step-friendly binary diffusion model structure to obtain a binary diffusion model; training the binary diffusion model by utilizing spatial block distillation to obtain a trained binary diffusion model; and generating an image task by using the trained binary diffusion model. According to the method, the collapse problem during image generation of a traditional binary diffusion model is solved.

The invention provides a text generation model training method and device, equipment and a storage medium. The method comprises the steps of obtaining a first text; determining first emotion feature information of the first text, and performing text reconstruction processing on the first text according to the first emotion feature information to obtain a second text; determining a training text according to the first text and the second text based on a preset text emotion prompt template; and training a to-be-trained text generation model according to the training text, and adjusting parameters of the to-be-trained text generation model according to a training result to obtain a target text generation model. According to the method, the training data volume required by the text generation model can be reduced, and the applicability of the trained text generation model can be improved.

The invention discloses a permanent magnet synchronous motor control system and method. The permanent magnet synchronous motor control system comprises a triangular carrier generator, a pulse width modulation module, a three-phase voltage source inverter and a permanent magnet synchronous motor. The permanent magnet synchronous motor control system further comprises a spread spectrum parameter optimization module, a first spread spectrum unit and a second spread spectrum unit. The spread spectrum parameter optimization module performs optimization on the first spread spectrum unit and the second spread spectrum unit through an immune algorithm; the first spread spectrum parameter unit extracts a transition probability based on a two-state Markov chain method to obtain a random parameter value S, and the second spread spectrum parameter unit obtains a randomized carrier frequency fs according to the random parameter value and a random gain; the triangular carrier generator obtains a triangular carrier signal of which the period changes randomly according to the randomized carrier frequency; the pulse width modulation module compares the triangular carrier signal with the modulated wave signal to obtain a three-phase pulse signal; the three-phase voltage source inverter processes the three-phase pulse signal and outputs a three-phase alternating current to provide power for the permanent magnet synchronous motor; according to the invention, the switching frequency and the harmonic amplitude at the frequency multiplication position are weakened, the technical effect of random spread spectrum modulation is improved, and the performance of the permanent magnet synchronous motor is further improved.

The present invention discloses a benzenesulfonamide compound, a pharmaceutical composition and application thereof. The compound of the present invention has the effect of promoting melanin production, has obvious effect and low toxicity, and can be used for preparing a drug for treating skin depigmentation or depigmentation-related diseases, wherein the skin depigmentation includes vitiligo, tuberous sclerosis, Ito hypopigmentation, mottled disease, oculocutaneous albinism syndrome, phenylketonuria, idiopathic punctate hypopigmentation, Comfetti spots, Vagabond's leukosis, etc.

The present invention provides a three-dimensional model motion migration method, which is characterized by: step S1, generating an action network model, a posture network model and an action posture model migrated to a new action according to pre-acquired three-dimensional model data as real values ​​for training an action migration neural network; step S2, constructing a feature encoding network as an action feature extractor to extract action features of an action three-dimensional mesh model; step S3, constructing a feature migration network as a decoder to perform a migration operation on the posture three-dimensional mesh model based on the extracted action features, thereby obtaining a deformed action posture model; step S4, constructing an end-to-end action migration neural network framework; step S5, constructing a loss function; step S6, training the action migration neural network based on the loss function, wherein the action migration neural network includes an action feature encoding network and a feature migration neural network based on a spatial adaptive instance normalization method.

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.

The present invention provides a method and system for automatic system testing of audit business processes. The method includes: an audit business process data indexing step, a large model training step, a large model test case script generation step, and a test case script integration running step. The present invention can understand a variety of specific audit business processes and rules by training a large model, so that the large model can automatically generate dynamic and flexible test case scripts, and simulate the user's audit business process under the Playwright framework for testing, thereby improving test efficiency, reducing labor costs, and improving test coverage and quality, thereby solving the above technical problems.

Embodiments of the invention disclose an animation generation method and apparatus, a computer device and a computer readable storage medium. The method comprises the steps of obtaining a virtual object model; determining a first model vertex and a second model vertex from the virtual object model according to the target deformation direction of the virtual object model; determining first motion data of the vertex of the first model according to the deformation control information; simulating a first model vertex based on the first motion data to drive a second model vertex to move to obtain second motion data of the second model vertex; and generating a deformation animation corresponding to the virtual object model based on the first motion data and the second motion data. According to the embodiment of the invention, the motion of the first model vertex and the second model vertex of the virtual object model is controlled in different modes, the first model vertex is controlled to move through the deformation control information, and the second model vertex is driven by the first model vertex, so that the generated deformation animation is more vivid and fine, and the animation generation effect is good.

The present invention discloses a method and system for processing image generation data based on artificial intelligence, the method comprising: obtaining design requirement data and basic text input by a target user; determining text processing requirements and background image requirements according to the design requirement data based on a prediction algorithm model; processing the basic text according to the text processing requirements to obtain processed text; generating a promotional design image based on the processed text and the background image requirements based on a GPT algorithm model. It can be seen that the present invention can effectively improve the efficiency of making promotional design images, reduce the time cost and labor cost caused by manual intervention, and generate promotional images with better effects.

The invention discloses application of a laurencia or a seaweed composition containing the laurencia as a feed additive in production of reducing methane and carbon dioxide in gastrointestinal tracts of ruminants, and application of the laurencia or the seaweed composition containing the laurencia in preparation of feed with the effect of reducing the methane and the carbon dioxide in the gastrointestinal tracts of the ruminants. The laurel algae combination is a combination of laurel and one or more of the following algae: gulfweed, enteromorpha, kelp, laver and sargassum fusiforme. According to the invention, the laurencia or the laurencia seaweed composition is added into the basal feed, so that the laurencia or the laurencia seaweed composition has a good effect of inhibiting generation of methane and carbon dioxide in gastrointestinal tracts of ruminants. According to the invention, the emission of methane or carbon dioxide of the ruminants is obviously reduced by adding the laurencia or the seaweed combination thereof into the basic ration of the ruminants, the feed utilization rate of the ruminants is improved, and the laurencia or seaweed combination has important value and significance in the aspects of green emission reduction, energy conservation, efficiency improvement and the like of the breeding industry, and has great industrial application potential.

The invention discloses a double-rare earth monatomic catalyst and application thereof in photocatalytic reduction of CO2. The preparation method of the catalyst comprises the following steps: dissolving urea, Er (NO3) 3 and Nd (NO3) 3 in deionized water containing melamine sponge, stirring at room temperature for 2-4 hours, and performing liquid nitrogen freezing and vacuum drying; and then under the protection of nitrogen, raising the temperature to 500-600 DEG C and keeping the temperature for 1-4 hours. The prepared catalyst takes g-C3N4 as a support body, and Er < 3 + > and Nd < 3 + > are respectively coordinated with N atoms in two tri-s-triazine rings. The obtained catalyst shows excellent C2H5OH and CH3OH generation capacity in a photocatalytic reduction CO2 performance test, the generation rates are 1761.4 and 987.7 [mu] mol g <-1 > h <-1 > respectively, the ethanol selectivity is up to 64%, and 98.4% of catalytic performance and initial morphology and structure are still kept after three times of circulation.

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.