145results about How to "Improve build quality" patented technology

The invention relates to an unmanned aerial vehicle knowledge graph construction method based on multi-modal large model recognition, and the method comprises the steps: carrying out the data fusion of texts, images, videos and sensor data related to an unmanned aerial vehicle, and constructing an unmanned aerial vehicle multi-modal data set; constructing a multi-modal large model integrated with a Transform architecture and a self-attention mechanism for extracting structured information from input data, and training the multi-modal large model by using a multi-modal data set of the unmanned aerial vehicle; structured information is extracted from unmanned aerial vehicle data input in real time by using the trained multi-modal large model to construct a knowledge graph, knowledge graph data is updated regularly through an online learning mechanism, and the quality and consistency of the knowledge graph are ensured by adopting an automatic verification and error correction mechanism. According to the method, the defects of low accuracy and low efficiency in unmanned aerial vehicle knowledge graph construction in the prior art are overcome, and the construction quality of the knowledge graph is improved.

The invention discloses a context learning-based database query generation method and system and a storage medium, and the method comprises the steps: extracting keywords and phrases from a natural language query, retrieving values similar to the keywords in a database, and returning values most related to the keywords and corresponding columns thereof, retrieving description information related to the keywords and the phrases in a database directory; performing mode selection aiming at the most relevant value of each keyword and the corresponding column, namely screening out the column irrelevant to the natural language query, further selecting a table necessary for generating the SQL query, and finally reducing the mode to a minimum column set necessary for generating the SQL query; and correcting logic and grammar errors of the method model through the database mode, the natural language query, the generated candidate SQL query and the execution result thereof. According to the method, the efficient SQL query statement is generated by intelligently understanding the data context and the query requirement.

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 relates to a catalyst for ethylene oxide ring-opening polymerization and a preparation method thereof, and belongs to the technical field of high polymer material synthesis, and the catalyst is a composite structure based on a silicon dioxide nanoparticle loaded bimetal organic complex; a modified nitrogen-containing polydentate ligand is prepared by adopting a special method, is coordinated with metal ions through covalent bonds and is loaded on silicon dioxide nanoparticles; by utilizing the high dispersity and stability of the silicon dioxide nanoparticles and the synergistic catalytic effect of the bimetal organic complex, the catalytic effect is effectively improved, the polymerization process is accelerated, the polymerization stability is enhanced, the polymerization molecular weight is improved, and the distribution is narrow.

The invention discloses a diffusion model self-guiding-based image or video generation method and system. The method comprises the steps that the input content is a noise potential variable and a future offset step length of a current time step; the diffusion model predicts the score network twice based on the input content to obtain a conditional score network of the current time step and the future offset time step; carrying out linear combination on the two conditional score networks to generate a combined conditional score network; denoising the noise potential variable of the current time step by using a combined condition score network; calculating and updating a potential variable of the previous time step according to a denoising result; in each time step, the self-guiding strategy is adopted to carry out denoising processing on the noise, and noise prediction is optimized; and after de-noising processing of all time steps, a de-noising potential variable is finally output as a final model generation result, and an image or a video is obtained. According to the invention, the quality of the generated image or video can be obviously improved.

The invention discloses a method for generating a multi-modal 3D object through mixed diffusion supervision, and the method comprises the following steps: S1, employing modal similarity loss to align modal prompt and rendering image coding, better injecting semantic information of multiple modals into the generation process of the 3D object, the 3D object generation module is used for enabling a generated 3D object to better meet the requirements of each modal prompt; s2, mixed diffusion supervision is adopted, priori knowledge of a 2D diffusion model and a 3D diffusion model is utilized, and the advantages of the two pre-training diffusion models are combined, so that the generation effect and consistency of a 3D object are improved; S3, a three-stage optimization method is utilized, mixed diffusion supervision is combined in each stage, and the 3D object is generated; according to the method, by adopting modal similarity loss, mixed diffusion supervision and three-stage optimization methods, the generation quality of the 3D object can be effectively improved, and the method has remarkable practical value and application prospect.

The invention discloses an assembly type multi-layer lightweight concrete sandwich wallboard structure system, which relates to an assembly type multi-layer building and comprises lightweight concrete sandwich prefabricated wallboards, vertical corner prefabricated members and fully prefabricated hollow floor slabs, the lightweight concrete sandwich prefabricated wall panel and the vertical corner prefabricated part are connected in an anchoring mode through a vertical anchoring connecting section, a prefabricated bracket is arranged on one side of the lightweight concrete sandwich prefabricated wall panel, and the full-prefabricated hollow floor slab is arranged on the prefabricated bracket. The fully-prefabricated hollow floor slabs and the lightweight concrete sandwich prefabricated wallboards are anchored and connected through the horizontal cast-in-place sections of the fully-prefabricated hollow floor slabs, and every two adjacent layers of lightweight concrete sandwich prefabricated wallboards and vertical corner prefabricated parts are fixedly clamped through buckle type connecting parts. The invention further discloses a construction method of the fabricated multi-layer lightweight concrete sandwich wallboard structure system. Connection joints are simple and convenient, grouting connection is not needed, the prefabricated horizontal members do not need temporary supports, the on-site pouring amount is small, and the stress performance is good.

The invention discloses a virtual fitting video generation method, a model training method, electronic equipment and a computer readable storage medium. The video generation method comprises the following steps: acquiring a human body video and a costume picture corresponding to target costume; extracting target clothing features and human body features corresponding to the video frames; fusing the target clothing feature with each human body feature in a spatial dimension; performing feature adjustment on the target clothing feature based on each human body feature to obtain each human body matching clothing feature matched with the human body in each video frame; and fusing each space fusion feature and each human body matching clothing feature in a time dimension to obtain a time fusion feature, and obtaining each human body try-on video frame on which the target clothing is tried according to the time fusion feature. Through the scheme provided by the invention, the generated virtual try-on video has better inter-frame continuity and fluency, so that the authenticity of the final virtual try-on video is improved, and the virtual try-on effect is better.

The invention provides a music generation model training method and device, equipment and a storage medium, and relates to the technical field of computers, in particular to the technical fields of artificial intelligence, large models, deep learning, music generation and the like. According to the specific implementation scheme, a video sample is input into a to-be-trained first music generation model, and a first video vector output by the first music generation model and a corresponding music marking sequence are obtained; generating a music file based on the music mark sequence; calculating a loss function according to the video sample, the music file, the first video vector and the music mark sequence; and training the first music generation model based on the loss function to obtain a trained second music generation model. According to the invention, the model can be guided to generate music matched with the video content, and the diversity and quality of music generation are improved.

The invention provides a test case generation method and device, a storage medium and electronic equipment, and relates to the technical field of artificial intelligence. The method comprises the following steps: acquiring agile work items, test demand information and application program editing interface information as background knowledge; inputting the background knowledge and the generation instruction into a first large model, and determining a test case scene; in response to adjustment of the test case scene, inputting the adjusted test case scene and background knowledge into the second large model, and determining a test case group; the test case group comprises a plurality of alternative test cases; the first large model and the second large model are dialogue type large models which are obtained through training and command prompt packaging; and in response to selection of a target test case from the test case group and editing of the target test case, determining an output test case. And the efficiency and the quality of test case generation are improved.

The present invention discloses a dynamic adaptive 3D model generation method based on deep learning, comprising: S1: constructing a data set; S2: constructing a 3D model generation model, importing the preprocessed image in S1 into a feature extraction module, and obtaining a multi-scale feature representation of the image; S3: importing the multi-scale feature representation in S2 into a feature fusion module for feature fusion, and obtaining fusion features; S4: importing the fusion features of S3 into a 3D point cloud generation module, obtaining multi-view image features to generate dense 3D point clouds; S5: a camera module performs feature point matching on the dense 3D point cloud in S4, and obtains a matching 3D point cloud; S6: a mesh generation module performs image generation on the matching 3D point cloud in S5 through a generative adversarial network, and obtains a corresponding 3D mesh, i.e., a final 3D model. The present invention can achieve a good balance between global structure and detail features by introducing an adaptive weighted fusion mechanism, and ensures the generation of high-precision 3D models in various scenarios.

The invention provides a pedestrian re-identification method based on three-dimensional reconstruction and image generation, and belongs to the technical field of machine vision. According to the invention, pedestrian images in an original pedestrian image set are utilized to generate a corresponding three-dimensional human body model; selecting a specific angle of the three-dimensional human body model to generate a two-dimensional pedestrian rendering graph; generating a virtual pedestrian image in combination with the two-dimensional pedestrian rendering graph and the original pedestrian image; adding the virtual pedestrian image into the original pedestrian image set to construct an enhanced image set; training a pedestrian re-identification model based on the enhanced image set; and inputting a to-be-detected pedestrian image to the trained pedestrian re-identification model to obtain a pedestrian identification result. According to the invention, the original pedestrian image set is enriched by generating images of different angles of portraits in the original pedestrian image set, and the problem of scarcity of pedestrian re-identification data is solved; and the model is trained and updated on the enhanced data set in combination with classification loss and triple loss, so that the model can correctly learn the generated image information, and the pedestrian re-identification precision is improved.

The embodiment of the invention provides a commodity information generation method and device, equipment and a computer program product. The method comprises the steps of obtaining a generation request of commodity information, wherein the generation request at least comprises category description corresponding to the commodity information and at least one knowledge item description; based on category description and at least one knowledge item description in the generation request, determining a commodity category matched with the generation request and concept attributes under each knowledge item under the commodity category; circle product analysis is carried out based on the commodity category and the concept attribute, commodity preliminary information is obtained, and the commodity preliminary information at least comprises recommended commodities corresponding to the generation request and crowd portraits corresponding to the recommended commodities; and based on the preliminary information of the commodity, generating commodity information corresponding to the generation request, the commodity information at least comprising description information of the commodity and a promotion document of the commodity. According to the embodiment of the invention, manpower and material resources required for generating the commodity information are reduced, and the generation quality and efficiency of the commodity information are improved.

The invention discloses a picture object detection method for an X-ray security inspection machine. The method comprises the following steps: acquiring a picture data set of the X-ray security inspection machine, generating a high-quality X-ray image sample through a generative adversarial network, expanding the data set, and optimizing a training process of an adversarial network generator through reinforcement learning. A multi-task learning model is constructed, object detection, object segmentation and object category identification are jointly trained, and under the condition of no labeled data, feature representation is generated through self-supervised learning to serve as an initial weight to be applied to training of an object detection model; the invention discloses a generative adversarial network. A traditional data enhancement method may have limitation in diversity and authenticity of generated samples. The complex condition in the actual environment can be better simulated through the sample generated by the GAN, so that the robustness of the model in the actual application is improved. And reinforcement learning optimization: utilizing reinforcement learning to optimize the training process of the GAN, and dynamically adjusting the generation strategy, so that the generated image samples are more diversified and real.

The invention discloses a three-dimensional model construction method and device, a storage medium and computer equipment, and relates to the technical field of financial science and technology and the technical field of digital medical treatment. Comprising the following steps: acquiring a two-dimensional image, and dividing the two-dimensional image into a plurality of panes; predicting a local approximate plane normal in a view angle range corresponding to each pane, determining a three-dimensional modeling starting point, determining a three-dimensional coordinate of the three-dimensional modeling starting point, taking the pane where the three-dimensional modeling starting point is located as a starting point pane, and taking the three-dimensional coordinate of the three-dimensional modeling starting point as a starting point center coordinate of a starting point local approximate plane corresponding to the starting point pane; determining a neighborhood pane adjacent to the starting point pane, and determining a neighborhood center coordinate of a neighborhood local approximate plane corresponding to the neighborhood pane based on the starting point center coordinate, a local approximate plane normal of the starting point pane and a local approximate plane normal of the neighborhood pane; and based on the starting point center coordinate and the neighborhood center coordinate, splicing the starting point local approximate plane and the neighborhood local approximate plane to obtain a three-dimensional model.

This invention relates to a method and system for automated generation of TSN network configurations based on a reflective agent. The system comprises a network state perception and intent reflection module, a YANG model configuration parameter and structure decision module, and a YANG model intelligent generation module. The network state perception and intent reflection module is responsible for structurally perceiving network state and resource information. The YANG model configuration parameter and structure decision module integrates multi-source knowledge and contextual information, intelligently generating deployable TSN scheduling parameters and YANG configuration structures based on business needs. The YANG model intelligent generation module automatically generates the corresponding YANG configuration model based on the optimized results, performs syntax and semantic verification, and then distributes it to the target device, achieving automated deployment of network configurations. This invention significantly improves the intelligence level and deployment automation capability of TSN network configuration by introducing reflective reasoning and feedback optimization techniques.

This invention relates to the field of decorative engineering technology, and discloses a method and system for generating virtual renderings of decorative engineering projects. The method includes: S1, parsing CAD drawings; S2, identifying professional intent; S3, defining and configuring key aesthetic elements; S4, attributing and correcting model parameters; S5, conflict detection; S6, adaptive fusion and correction; S7, material mapping and lighting calculation; and S8, outputting the virtual rendering. This invention solves the problem that the complexity of CAD drawings leads to incomplete or inaccurate geometric structures in 3D models, which in turn affects material mapping and lighting calculation, thus impacting the visual realism of virtual renderings.

The invention provides an automatic construction method of a structured document title, which can automatically generate a title for a structured document with a long context. The method comprises the following steps of: 1, generating an abstract of a structured document by utilizing structural information and content characteristics of the document through an abstract generation module based on a hierarchical attention network; 2, generating keywords from the abstract by using a text generation model based on a bidirectional encoder and an autoregression decoder, and preferably selecting the keywords through a keyword screening mechanism; thirdly, interaction between the screened keywords and the abstract is analyzed through a cross attention mechanism, and feature representation fusing keyword features and document content is generated; and 4, automatically constructing a structured document title according to the feature representation. The whole process is automatically completed, and the title related to the structured document content can be obtained.

The invention discloses an unmanned driving scene generation method based on a StyleGAN-V algorithm, and the method comprises the implementation steps: firstly, collecting a real severe weather driving scene video, and carrying out the preprocessing of the video, and constructing a data set; then preliminarily constructing a video generation model; a time sequence feature extraction module combining time sequence excitation and time sequence displacement is constructed, and time sequence features of the video are extracted; introducing a joint loss function constraint in the generation process; and finally, calculating an improved loss function, updating video generation model parameters, and continuously training until the generation model is completed. According to the method, the driving scene in the severe weather can be realistically simulated, and through experimental verification, the method can effectively improve the continuity of the generated video and the capturing capability of dynamic characteristics, so that the authenticity of the driving simulation scene in the severe weather is improved, the sensing capability of subtle action changes in the video is improved, and the driving experience is improved. And better performance is achieved in the aspects of dynamic characteristics and picture continuity of the video.

This invention discloses a method for generating poses of articulated objects based on physical perception and graph diffusion, relating to the field of computer vision. The method first reconstructs the componentized geometric and physical properties of objects from images using a bi-branch neural implicit network and initializes component connectivity relationships. Subsequently, it refines the component relationship graph through kinematic fitting and temporal consistency checks, and uses a physically enhanced graph diffusion process to infer the prior pose distribution of components on an SE(3) manifold. Finally, using this prior and reconstructed information as conditions, a conditional diffusion model is constructed on the SE(3) manifold, and backsampling is performed through a physically guided two-step backsampling framework to generate a diverse and physically plausible set of pose assumptions for articulated objects. This invention achieves efficient generation of diverse and highly physically plausible poses of articulated objects by tightly coupling physical laws with data-driven generation.

The invention discloses an underground powerhouse axis direction selection method adopting a knowledge graph and a block theory and a readable storage medium, and the method comprises the steps: S1, constructing an underground powerhouse axis knowledge graph: selecting factors through the axis direction of an underground powerhouse, determining entity object feature indexes, collecting and processing an entity attribute database, and constructing a knowledge graph; and mapping the entity attribute data and the entity relationship to nodes and edges of the knowledge graph, and establishing the knowledge graph. S2, sorting underground powerhouse area survey data, setting sporophores and attribute data of each entity object, and obtaining underground powerhouse axis entity objects and attributes through a knowledge graph; and S3, in combination with a block theory, selecting a proper axis direction of the underground powerhouse. The problems that in the prior art, a traditional arrangement scheme mainly depends on artificial experience and professional knowledge, and a systematic and scientific method is lacked to support the decision making process are solved, and the decision making efficiency is improved.

The present application discloses a method, device, equipment and storage medium for audio and video lip translation, which relates to the field of video translation, including: determining the motion trajectory of each character ID to determine the audio to be translated corresponding to each character ID; performing speech recognition on the audio to be translated to obtain the text to be translated and the current translated text to generate the current translated audio, and judging whether the audio duration of the current translated audio meets the preset re-translation condition; if it meets, re-translating to obtain the new current translated text, and jumping to the step of generating the current translated audio; if it does not meet, audio alignment is performed on the current translated audio based on the audio to be translated to obtain the target translated audio; synthesizing each target translated audio to obtain the target audio, generating each lip picture and generating the target video based on the target audio and reference frame, and merging the target video with the target audio to obtain the translated audio and video. The present application realizes lip translation of the audio and video to be translated.

This application relates to the field of data processing technology, and in particular to a method, apparatus, device, and storage medium for model training and 3D mesh reconstruction, used to rapidly generate high-quality 3D results. The model training method includes: obtaining an initial repair model by distilling a student model to be trained; the initial repair model is used to make the input initial sample set, after M iterative repairs, present the shape of a 3D point cloud corresponding to the initial sample set; obtaining an intermediate repair model by distilling the initial repair model; the intermediate repair model is used to make the input intermediate sample set, after N iterative repairs, present the shape of a 3D point cloud corresponding to the intermediate sample set; and obtaining a target repair model by adversarial training of the intermediate repair model based on the initial repair model, so that the error between the results obtained by repairing the same target sample set using the target repair model and the initial repair model conforms to a preset error range.

The suction tube temporary pile shoe structurally comprises a top platform, a groove is formed in the center of the top platform, four lifting lugs are evenly distributed on the top of the top platform in the circumferential direction, and a gravel layer is laid in the groove of the top platform; a suction cylinder group is arranged at the bottom of the top platform, and N pump skid bodies are movably arranged on the gravel layer of the top platform and are respectively matched with four suction cylinders in the suction cylinder group so as to drive the four suction cylinders to sink to a parking state or float upwards to be in a movable state; the structure meets the parking requirement of the platform ship and can be selectively moved to a designated machine position along with the workboat, and repeated parking is achieved. Compared with an existing platform ship parking mode, according to the two wind power operation methods correspondingly achieved through the structure, pile legs of the platform ship do not need to be inserted into mud any more, the requirement for a platform ship lifting system is lowered, the problem that the construction efficiency is low due to pile washing and ballast water adjusting is solved, and the offshore wind power construction efficiency and quality are improved.

This invention relates to the field of artificial intelligence technology, and particularly to an automated fuzzy appeal processing method and system based on multi-agent intelligence. This automated fuzzy appeal processing method based on multi-agent intelligence obtains the probability distribution of fuzzy information in a user's appeal through a preset size model recognition strategy; determines the information entropy of multiple words in the user's appeal based on the fuzzy information probability distribution, and identifies target fuzzy information sites by identifying words whose information entropy exceeds a preset information entropy threshold and their corresponding positions in the user's appeal; executes a disambiguation strategy on the target fuzzy information sites, and performs long-span coarse-grained task planning for the disambiguated user based on multi-agent intelligence, forming a task heterogeneity graph; constructs task execution meta-paths based on the task heterogeneity graph, and processes user appeals according to the task execution meta-paths. This achieves automated fuzzy appeal processing from fuzzy information recognition, disambiguation, and post-disambiguation task planning to final execution, as well as accurate recognition of fuzzy information.

The invention discloses a test case generation model training method and a related device, and relates to the field of artificial intelligence. The method comprises the steps of obtaining a preset sample script, a first training set and a training labeling test case corresponding to the first training set; inputting the sample script and the first training set into a test case generation model, and obtaining a plurality of first test cases through the test case generation model; in order to detect whether the first test case generated by the test case generation model lacks some necessary objects, the first test case can be utilized to test the application program. Determining a second test case which has errors in the first test case which fails to be tested due to the test case; based on the test failure reason of the second test case, obtaining a first Prompt cue word; the test case generation model obtained by training the first Prompt prompt word again is more accurate, the test case is obtained through the test case generation model, manual writing is not needed, and the efficiency is improved.

Embodiments of the present application disclose a risk behavior detection method, device and equipment, the method comprises: determining a first scene with a behavior correlation between the target scene is less than a preset correlation threshold, obtaining the first picture data containing the target behavior corresponding to the target scene, the target behavior annotation information corresponding to the target behavior in the first picture data, and the second picture data containing the first behavior corresponding to the first scene, based on the second picture data and the target behavior annotation information, the first sample data is constructed, based on the first picture data, the target behavior annotation information, and the first sample data, a preset visual language model is trained, and a trained visual language model is obtained, the trained visual language model is used for detecting whether a preset risk behavior exists in the video to be detected.

The present invention discloses an image device data management method and system based on cloud-edge collaboration, the method comprising: a cloud server configures task information and workflow information for an edge server and an edge device, and sends the task information and workflow information to the edge server; the edge server receives and parses the task information and workflow information, and sends a data acquisition instruction to the edge device; the edge device performs data acquisition according to the data acquisition instruction, obtains image data, and transmits the image data back to the edge server; the edge server processes the image data according to the workflow information, and uploads the processed image data to the cloud server; the cloud server receives and manages the image data according to a preset receiving mechanism, and constructs the received image data into an image device data packet. The present invention constructs an image device data packet based on cloud-edge collaboration, which can effectively improve the quality of image data.

The invention relates to the technical field of image processing, and discloses a landslide surface crack three-dimensional evolution unmanned aerial vehicle image extraction and quantification method and system, and the method comprises the steps: carrying out the bundle adjustment of an ultrahigh overlapping degree aerial image of a landslide surface, and obtaining an image shooting position, an image attitude parameter and a sparse three-dimensional point cloud; constructing a high-precision digital earth surface model; performing digital mosaic on the aerial image with the ultrahigh overlapping degree to obtain an orthoimage; performing semantic segmentation on the orthoimage to obtain a crack binary segmentation map of the orthoimage so as to identify a main crack and a secondary crack; performing three-dimensional geometric parameter analysis on the main crack and the secondary crack to obtain a crack three-dimensional space attribute parameter; carrying out evolution trajectory tracking on the crack three-dimensional space attribute parameters to obtain evolution data so as to construct a visual map of the landslide surface; according to the invention, the efficiency of extracting and quantifying the three-dimensional evolution unmanned aerial vehicle image of the landslide surface crack can be improved.

The invention discloses a document generation method and device, equipment and a medium, and relates to the field of artificial intelligence, and the method comprises the following steps: obtaining a document theme which is used for indicating theme content of a to-be-generated first document; obtaining a plurality of sample documents and a plurality of data contents, wherein the plurality of sample documents meet preset document quality requirements; determining at least one sample document from the plurality of sample documents based on the document topic, and determining a first data content from the plurality of data contents based on the document topic; performing format conversion on the first data content to obtain first chart content; analyzing the first chart content through a first natural language model, and outputting to obtain a first text content for describing the first chart content; analyzing the document theme, the at least one sample document, the first chart content and the first text content through the second natural language model, and outputting to obtain a first document. The document generation efficiency and the document quality can be improved.