8results about How to "Improve refactoring efficiency" patented technology

The application discloses a reconstruction method and system for a cavity under a shallow reinforcement net shelter, and comprises the following steps: constructing a layered medium scene containing a shallow reinforcement net and a cavity; acquiring image data information with different shelter states and preprocessing to construct a reconstruction data set; constructing a reconstruction initial model for the cavity under the shallow reinforcement net shelter and training to obtain a reconstruction model for the cavity under the shallow reinforcement net shelter; and using the obtained reconstruction model for the cavity under the shallow reinforcement net shelter to perform actual reconstruction for the cavity under the shallow reinforcement net shelter. The application acquires multiple types of training data through the constructed layered medium scene containing the shallow reinforcement net and the cavity, and trains the reconstruction model for the cavity under the shallow reinforcement net shelter which comprises a U-shaped network, a Sobel operator, an attention mechanism, a residual structure, an average mechanism and a skip connection scheme. Therefore, the application can not only realize the reconstruction for the cavity under the shallow reinforcement net shelter, but also has higher reliability, better accuracy and higher reconstruction efficiency.

This invention provides a conflict-aware transactional reconstruction method and apparatus for vehicular networks, belonging to the field of vehicular network configuration and scheduling control technology. It solves problems such as multi-node time slot conflicts, cross-device submission asynchrony, and inability to quickly roll back after update failures in existing vehicular network transactional reconstruction processes. The method includes: upon receiving a reconstruction request, generating a set of scheduling update operations based on the reconstruction request and the latest scheduling status table of each device node in the vehicular network; establishing a conflict graph and a dependency graph based on the scheduling update operation set; determining a set of transaction execution layers and the corresponding hierarchical execution sets for each transaction execution layer based on the conflict graph and dependency graph; and performing transactional reconstruction on each device node based on the set of transaction execution layers and the corresponding hierarchical execution sets, including differential patch pre-writing, atomic pointer switching triggered by a unified timestamp, and distributed consistency monitoring.

The application provides a driving simulation system, comprising various types of driving simulators, a modular graphics rendering chassis and a central processing unit; the modular graphics rendering chassis comprises a chassis body, and a plurality of pluggable lane graphics rendering board cards and lane graphics rendering board card switching circuits arranged in the chassis body; the lane graphics rendering board card switching circuits are electrically connected with the central processing unit and each lane graphics rendering board card respectively, and the image signal output ends of each lane graphics rendering board card are electrically connected with each type of driving simulator. Through a plurality of preset physical level lane graphics rendering board cards, the lane graphics rendering board card switching circuit can quickly switch to different lane graphics rendering board cards, realize the quick switching and combination of various tunnel scenes, and greatly improve the tunnel scene reconstruction efficiency.

The embodiment of the invention relates to a knowledge editing method and device based on reasoning chain condition constraint, and the method comprises the steps: defining four types of condition constraint rules and a candidate instruction node graph for a target model, and constructing four types of discriminators for the four types of rules; docking the user-specified knowledge base with the target model; each task problem of the user task set is substituted into the instruction template to generate a multi-step reasoning task instruction, the multi-step reasoning task instruction is input into the target model to carry out one-time multi-step reasoning task processing, and one-time task pause is carried out every time one-step reasoning is completed in the processing process; and then updating the node graph according to the current inference step data, performing constraint condition judgment on the current inference output by utilizing four types of discriminators, continuing the next inference when the constraint condition is met, and performing available node retrieval on the node graph when the constraint condition is not met, reconstructing an inference chain based on a retrieval result, and performing inference again according to a reconstruction chain. The knowledge learning precision of the target model can be improved, the knowledge learning deviation is reduced, and the knowledge editing efficiency is improved.

The application discloses a kind of multi-viewpoint fast three-dimensional reconstruction method and system, the method includes based on the effective physical observation range of current job station environment setting 3D camera to target object;The first frame image of target object obtained is converted into first point cloud data;Second frame image of the next best view angle obtained based on the space grid model constructed from first point cloud data is converted into second point cloud data;Based on the spatial real pose transformation matrix of default initial view angle and next best view angle, rough registration is completed;The three-dimensional reconstruction model of target object is generated with the spatial real pose transformation matrix as initial value;The multi-viewpoint fast three-dimensional reconstruction method and system, adaptive adjustment, active viewpoint planning, and introduce robot kinematics prior data to obtain the spatial pose transformation matrix of camera in real time, and the matrix is directly used as the good initial pose of two frame point cloud registration, solve the problem of lack of initial pose of existing algorithm.

The application discloses a kind of based on deep feature fusion's cable membrane structure digital twin model reconstruction method, including survey entity cable membrane space structure, arrangement acquisition station, acquisition cable membrane space structure three-dimensional point cloud data;The three-dimensional point cloud of cable membrane space structure is preprocessed and constructs dataset;RPMNet network architecture is improved, and spatial attention mechanism method is applied to the point cloud feature extraction layer of RPMNet network architecture;The three-dimensional point cloud of cable membrane space structure is finely registered;Based on the three-dimensional registration point cloud of cable membrane space structure, fine model reconstruction is carried out;Deviation analysis is carried out to the fine model reconstruction of cable membrane space structure and theoretical design model.The method realizes the accurate registration of three-dimensional point cloud data of cable membrane space structure, completes the high-fidelity digital twin model reconstruction of entity cable membrane structure space form, has the characteristics of high efficiency, low cost and wide coverage, has good applicability to cable membrane space structure, and can be widely applied to engineering practice.

The invention discloses a code reconstruction method and device, equipment, a medium and a chip, and belongs to the field of artificial intelligence. Comprising the steps of generating a UML class diagram according to a call chain of a source code of a to-be-upgraded application; the UML class diagram is a code class relation diagram meeting the reconstruction requirement of the source code; performing function marking on a plurality of functions contained in the source code to obtain function marking information; generating at least one code reconstruction request according to the source code, the reconstruction demand of the source code and the function mark information; a target code is obtained through a pre-trained code reconstruction model according to the code reconstruction request, and the code reconstruction model is obtained through training based on a GPT large language model: a target code framework is obtained through the pre-trained code reconstruction model according to the UML class diagram and the function marking information; and splicing the target code with the target code framework to obtain a reconstructed code of the source code.

This invention provides a satellite constellation simulation system and its reconfiguration method, solving the problem of excessively long constellation reconfiguration time. The method includes: periodically sending clock synchronization signals to each satellite model; for each satellite model in the satellite constellation simulation model: determining whether a clock synchronization signal has been received; if so, obtaining the operating status of all satellite models from the state pool; determining whether the current satellite model needs reconfiguration based on the operating status of all satellite models; if so, reconfiguring the current satellite model; calculating the difference rate between the reconfigured satellite model and the satellite constellation simulation model; forming a difference rate control pool based on the difference rate, the difference rate control pool including satellite ID, time / rate flag, acceleration time, and difference rate; and obtaining the difference rate corresponding to the current satellite model from the difference rate control pool after reconfiguration, configuring the difference rate as the simulation rate of the current satellite model.