121results about How to "Improve build quality" patented technology

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 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 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 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.

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.

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 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.

The application discloses a radar target recognition method and device with azimuth enhancement under physical scattering constraints. The core is that a feature level azimuth expansion module (FAEM) is constructed, sparse view angle features are augmented in a feature space, and effective expansion of azimuth information is realized; meanwhile, a scattering center reconstruction constraint module (SCRCM) is innovatively introduced, physical authenticity and semantic fidelity of generated features are improved; in addition, multi-view perception enhancement modules (MVFM) are used to fuse generated multi-azimuth features, and intra-class compactness constraints are applied to optimize feature space structures, and the recognition ability of the model is further improved. The method realizes optimization from feature generation, physical constraints to semantic enhancement, and effectively improves the target recognition performance under sparse view angles.

This invention relates to the technical field of programming task code generation methods based on multi-agent collaboration and iterative reasoning. Specifically, it discloses a method for generating programming task code based on multi-agent collaboration and iterative reasoning, comprising: obtaining a task description with a primary-secondary functional dependency structure; generating initial code by a generating agent based on a dependency-guided thought chain and performing static structure verification; collecting runtime execution trajectories in an isolated sandbox through dynamic agents; having a diagnostic agent attribute errors to the trajectories, distinguishing between inter-function collaboration errors and intra-function logic errors, and generating structured repair guidance; and combining historical perception feedback and a controlled reset mechanism to guide subsequent iterative repairs and avoid local optima. This invention solves the problems of ambiguous error location, easy code degradation, and low repair efficiency in existing code generation methods for multi-functional dependency tasks, achieving accurate error attribution, preventing code rollback, and improving generation success rate and repair convergence efficiency.

The application relates to an advertisement script generation method and device, equipment and medium in the computer technology field, which comprises the following steps: adopting a text generation model to generate a product text of an advertisement product, and predicting a generation probability distribution corresponding to each advertisement word element mapped to an encoding word table. Each generation probability distribution is weighted by a category probability distribution corresponding to the product category of the advertisement product to obtain a corresponding candidate probability distribution. According to each candidate probability distribution, a plurality of candidate word elements are selected from the encoding word table, and advertisement phrases starting with each candidate word element of a shop corresponding to the advertisement product are selected from a historical advertisement phrase library as candidate words to construct a corresponding candidate word set. For each advertisement word element, corresponding words are determined in the candidate word set or in the encoding word table according to the generation probability distribution / candidate probability distribution to construct an advertisement script. The application can generate high-quality advertisement scripts.

The application discloses a behavior tree automatic generation method and device based on a large language model and electronic equipment, relates to the technical field of artificial intelligence and automatic control, and comprises the following steps: acquiring task description information, constructing a node variable context object, injecting a structured prompt word, and forming a resource constraint-aware generation context; according to a semantic vector of the task description, searching for high-quality cases that meet a quality threshold, screening dynamic few-sample examples through a selection algorithm, and embedding generation prompt words; calling a large language model deployed locally to generate a behavior tree, performing syntax checking and logic defect detection on the generation result through an evaluation operator, converting a detection failure result into structured feedback information, driving the model to perform minimum iteration refinement until the checking is passed or the maximum number of iterations is reached. The application solves the problems of low availability, poor stability and high deployment cost of the large language model in generating the behavior tree, and enables a lightweight large language model to achieve a generation success rate of about 90% on consumer-grade hardware.

The invention discloses a steel-concrete composite beam cable-stayed bridge girder deflection calculation method considering interface slippage deformation. Comprising the following steps: calculating the slippage of the main beam section of the steel-concrete composite beam cable-stayed bridge, analyzing the deflection of the main beam of the steel-concrete composite beam cable-stayed bridge considering the interface slippage effect, and performing equivalent analysis on the mechanical behavior of the main beam in the steel-concrete composite beam cable-stayed bridge through a multi-span continuous system. According to the method, the deflection of the main beam of the steel-concrete composite beam cable-stayed bridge in the construction and operation stages can be accurately predicted, important technical support is provided for design, construction monitoring and operation maintenance of a bridge, and the construction quality and safety of bridge engineering can be improved.

This invention provides a method, apparatus, device, and readable storage medium for improving audio generation quality, relating to the field of speech conversion and recognition technology. The method includes acquiring low-sampling-rate audio, a target audio sampling rate, and an audio processing model; calculating an initial high-sampling-rate audio based on the low-sampling-rate audio, the target audio sampling rate, and the audio preprocessing mathematical model; calculating the target audio time-domain signal and target audio wavelet coefficients based on the initial high-sampling-rate audio and an audio signal reconstruction mathematical model; and solving the mathematical model based on the target audio time-domain signal, the target audio wavelet coefficients, and a preset fused audio signal mathematical model to obtain the target high-sampling-rate audio. This invention uses discrete wavelet transform to capture multi-scale details of the audio signal and reconstructs high-sampling-rate audio by combining the time-domain and frequency-domain information of the audio signal, further improving the overall generation quality of the audio.

The application discloses a ship body segmentation splicing method based on a laser point cloud device, and comprises the following steps: acquiring X partial point cloud data of a ship body segmentation; splicing the X partial point cloud data by using the spatial overlapping part and the same features of the point cloud data; deleting the point cloud data of a part which is not needed to be compared with a ship body segmentation design model, and reserving the ship body segmentation point cloud; generating a target point by the key position of the ship body segmentation point cloud data, performing coordinate conversion on the coordinates of the target point and the corresponding position of the ship body segmentation design model, converting the coordinate system of the ship body segmentation point cloud data into the same as the theoretical coordinate system of the ship body segmentation design model; outputting the required point cloud format, saving the complete ship body segmentation point cloud data, and realizing the ship body segmentation splicing. The precision and efficiency of the segmentation splicing directly determine the linear smoothness, structural strength, assembly stress, construction period and cost of the final ship body, and is one of the core key processes of ship construction.

The embodiment of the invention relates to a processing method and device for retrieving an enhanced language model based on a variational auto-encoder architecture. The method comprises the following steps: introducing a low-rank enhancer for a basic model to obtain a basic revision model; performing data acquisition on the public question and answer data set to obtain a first data set training basic revision model; constructing a second data set according to the target text library after training is finished, and combining the first data set and the second data set to obtain a third data set; constructing a feature vector library of the target text library; a retriever and a feature aggregation module are introduced into the basic revision model to form a retrieval enhancement language model, and the retrieval enhancement language model is in butt joint with the feature vector library; training a retrieval enhancement language model based on the third data set; and after the training is finished, processing a natural language question and answer task by using a retrieval enhanced language model. The quality of the generated text can be improved.

This invention discloses a collaborative update method for knowledge bases based on multi-source corpora, belonging to the field of knowledge base update technology. The method includes: acquiring incremental text streams from multiple heterogeneous corpora and segmenting them into text segments to be verified according to time windows; extracting the terminology dependency paths of the text segments to be verified and mapping them to candidate update triples for the target knowledge base; collecting existing triples in the knowledge base that have the same head entity as the candidate triples and extracting their tail entity type distribution; determining the conflict level based on the type offset between the tail entity type of the candidate triples and the existing distribution; directly entering the triples into the review queue when the conflict level is below a threshold; recalling a set of supporting texts from the heterogeneous corpora, correcting the relational predicates based on co-occurrence frequency, and then sending them into the review queue; and writing each triple into the main storage area of ​​the knowledge base in timestamp order. This invention achieves adaptive conflict handling in incremental knowledge base updates, effectively balancing update efficiency and knowledge consistency.

The application discloses a kind of T profile intersection panel double-angle transition operation methods, and the method specifically includes the following steps: S1, web in T profile is respectively welded and fixed with bulkhead and outer plate, and the circular radius R of arc angle transition is measured;S2, the panel of T profile on bulkhead is installed on the web on bulkhead, and the panel width B is measured;S3, the panel of T profile on outer plate is installed on the web on outer plate, and the angle A of panel and web on outer plate is measured;S4, make dome long circle bottom platform device, according to the circular radius R cutting dome long circle bottom platform, obtain the transition panel of arc angle transition;S5, the transition panel is installed on the web;S6, the transition panel is cut to panel excess after installation is completed, and the installation of bulkhead and outer plate transition panel is completed.The application is cut by making dome long circle bottom platform to realize the manufacture and installation of transition panel, improve the installation efficiency of transition panel and construction quality.

The invention discloses an efficient and automatic construction method for Tibetan and Chinese bilingual parallel corpora, and relates to the technical field of Tibetan and Chinese bilingual parallel corpora construction.The Tibetan and Chinese bilingual parallel corpora construction comprises a base model unit, a localization processing unit and a Tibetan stability strengthening module, the base model unit is a Qwen2.5 series pre-training model as a technical base, and the localization processing unit is a local processing unit; the training model comprises a 70B parameter main generation model and a 7B parameter lightweight intention judgment model, and the 70B model is responsible for receiving an input text and generating Tibetan-Chinese bilingual parallel sentence pairs. A base model unit, a localization processing unit and a Tibetan stability strengthening module communicate with one another through shared memory communication, the Tibetan stability strengthening module further comprises a double-model cooperation mechanism, and the working process of the double-model cooperation mechanism comprises the steps that input Chinese sentences are preprocessed through a word segmentation module, 7B model execution intention triad analysis is carried out, and the Chinese sentences are classified into Chinese sentences; and semantic control codes are generated, so that the output stability of the Tibetan and Chinese bilingual parallel corpora is improved through a double-model collaborative architecture.